Vehicle_detection_and_counting_in_traffi.pdf

Vehicle detection and counting in traffic video on highway highway

CHAPTER-1 INTRODUCTION The result of the increase in vehicle traffic, many problems have appeared. For example, traffic accidents, traffic congestion, traffic induced air pollution and so on. Traffic congestion has been a significantly challenging problem. It has widely been realized that increases of preliminary transportation infrastructure, more pavements, and widened road, have not been able ab le to relieve city congestion. As a result, many investigators have paid their attentions on intelligent transportation system (ITS), such as predict the traffic flow on the basis of monitoring the activities at traffic intersections for detecting congestions. To better understand traffic flow, an increasing reliance on traffic surveillance is in a need for better vehicle detection at a wide-area. Automatic detecting vehicles in video surveillance data is a very challenging problem in computer vision with important practical applications, such as traffic anal ysis and security. Vehicle detection and counting is important in computing traffic congestion on highways. The main goal Vehicle detection and counting in traffic video project is to develop methodology for automatic vehicle detection and its counting on highways. A system has been developed to detect and count dynamic vehicles efficiently. Intelligent visual surveillance for road vehicles is a key component for developing autonomous intelligent transportation systems. The entropy mask method does not require any prior knowledge of road feature extraction on static images. Detecting and Tracking vehicles in surveillance video which uses segmentation with initial background subtraction using morphological operator to determine salient regions in a sequence of video frames. Edges are be counting which shows how many areas are of particular size then particular to car areas is locate the points and counting the vehicles in the domain of traffic monitoring over highways. Automatic detecting and tracking vehicles in video surveillance data is a very challenging problem in computer vision with important practical applications, such as traffic analysis and security. Video cameras are a relatively inexpensive surveillance tool. Manually reviewing the large amount of data they generate is often impractical. Thus, algorithms for analyzing video

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which require little or no human input is a good solution. Video surveillance systems are focused on background modeling, moving vehicle classification and tracking. The increasing availability of video sensors and high performance video processing hardware opens up exciting possibilities for tackling many video understanding problems, among which vehicle tracking and target classification are very important. A vehicle tracking and classification system is described as one that can categorize moving vehicles vehi cles and further classifies the vehicles into various classes. Traffic management and information systems depend mainly on sensors for estimating the traffic parameters. In addition to vehicle counts, a much larger set of traffic parameters like vehicle classifications, lane changes, etc., can be computed. Vehicle detection and counting uses a single camera mounted usually on a pole or other tall structure, looking down on the traffic scene. The system requires only the camera calibration parameters and direction of traffic for initialization. Two common themes associated with tracking traffic movement and recognizing accident information from real time video sequences are

 The video information must be segmented and turned into vehicles.  The behavior of these vehicles are monitored (they are tracked) for immediate decision making purposes.

1.1 Problem Definition Current approaches of monitoring traffic include manual counting of vehicles, or counting vehicles using magnetic loops on the road. The main drawback of these approaches, besides the fact that they are expensive, is that these systems only count. The current image processing method uses temporal differencing method this fail in complete extraction of shapes of vehicle, in feature-based tracking method extracted image is blur, active contour method is very difficult to implement and in region-based tracking is very time consuming, so in this project Adaptive Background subtraction method for detection and ostu’s method to method to overcome these all problems.

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1.2 Scope of the Project The main purpose Vehicle Vehicle detection and counting from video sequence method is to Tracking and counting vehicles for High quality videos and also identify the colure of each vehicle.

1.3 Objectives 

Detection of multiple moving vehicles in a video sequence.

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Tracking of the detected vehicles.

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Colure identification of Vehicles.

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Counting the total number of vehicles in videos.

1.4 Literature survey 1. Gupte S., Masoud O., Martin R. F. K. and Papanikolopoulos N. P., proposed ―Detection ―Detection and Classification Vehicles‖ Vehicles‖ in the March, 2002, The presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, region level, and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. Briefly describe an interactive camera calibration tool that is developed for recovering the camera parameters using features in the image selected by the user.

2. Toufiq P., Ahmed Egammal and Anurag Mittal, proposed ―A Framework for Feature Selection for Background Subtraction‖, Subtraction‖, in 2006. in 2006. Background subtraction is a widely used paradigm to detect

moving vehicles in

video taken from a static camera and is used for various important applications such as video surveillance, human motion analysis, etc. Various statistical approaches have been Dept. of CS&E

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proposed for modeling a given scene background. However, there is no theoretical framework for choosing which features to use to model different regions of the scene background. They introduce a novel framework for feature selection for background modeling and subtraction. A boosting algorithm, namely Real Boost, is used to choose the best combination of features at each pixel. Given the probability estimates from a pool of features calculated calculate d by Kernel Density Estimate (KDE) over a certain time period, the algorithm selects the most useful ones to discriminate foreground vehicles from the scene background. The results show that the proposed framework successfully selects appropriate features for different parts of the image.

3. Toufiq P., Ahmed Ahmed Egammal and Anurag Mittal, proposed ―A Framework for Feature Selection for Background Subtraction‖, In 2006. Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveillance, human motion analysis. Various statistical approaches have been proposed for modeling a given scene background. However, there is no theoretical framework for choosing which features to use to model different regions of the scene background. The paper introduces a novel framework for feature selection for background modeling and subtraction. A boosting bo osting algorithm, namely Real Boost, is used to choose the best combination of features at each pixel. Given the probability estimates from a pool of features calculated by Kernel Density Estimate (KDE) over a certain time period, the algorithm selects the most useful ones to discriminate foreground objects from the scene background. The results show that the proposed framework successfully selects appropriate features for different parts of the image.

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CHAPTER-2 SOFTWARE AND HARDWARE REQUIREMENT SPECIFICATION The software and hardware requirement for the ve hicle detection and counting method is as follows:

2.1 Hardware requirements requirements 

Processor

:

Pentium IV onwards



RAM

:

2 GB or higher



Hard Disk Space

:

20 GB or higher



Input

:

Video Sequence

2.2 Software requirements requirements 

Operating System

:

Windows XP or higher



Software

:

Matlab 7.10 (R2013a)

MATLAB (matrix laboratory): generation

programming

language.

is

a numerical MATLAB

computing environment and allows matrix manipulations,

fourth plotting

of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, and Fortran. Image processing Toolbox provides a comprehensive set of reference-standard algorithms, functions, and apps for image processing, analysis, visualization, and algorithm development. users can perform image enhancement, image deblurring, feature detection, noise reduction, image segmentation, geometric transformations, and image registration. Many toolbox functions are multithreaded to take advantage of multicore and multiprocessor computers. Image Processing Toolbox supports a diverse set of image types, including high dynamic range, giga pixel resolution, embedded profile, and tomographic. Visualization functions let

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users explore an image, examine a region of pixels, adjust the contrast, create contours or histograms, and manipulate regions of interest (ROIs). With toolbox algorithms users can restore degraded images, detect and measure features, analyze shapes and textures, and adjust color balance.

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CHAPTER-3 HIGH LEVEL DESIGN 3.1 Design consideration In the design consideration the high level design of our project it includes four modules, for this architecture pattern is as follows

3.2 Architecture Design The figure3.1, gives an overview of the moving vehicle detection in a video sequence. The system makes use of an existing video sequence. The first frame is considered as the reference frame. The subsequent frames are taken as the input frames. They are compared and the background is eliminated. If a vehicle is present in the input frame, it’ll be retained. The detected vehicle is thus tracked by various techniques, namely, adaptive background method and blob analysis method.

Input Frames

Segmentation

Detection

Tracking

Counting

Figure: 3.1 overview of vehicle detection and counting system. In the adaptive background subtraction algorithm, assume that the first frame is background for the video clips considered. The architecture of the proposed p roposed algorithm is shown in Figure3.1. The flow of the algorithm for background elimination is as shown in figure3.2 Video clip is read and it is converted to frames. In the first stage difference between frames are computed i.e. FR1and FR1+j. In the next stage these differences are compared, and in the third stage pixels having the same values in the frame difference are eliminated. The fourth phase is the post processing stage executed on the image obtained in third stage and the fifth phase is the vehicle detection .and vehicle tuning .And final stage is counting vehicles.

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Input video clip

FR1

FR1+ j

Background registration

Image subtraction

Foreground detection

Image segmentation

Vehicle detection

Vehicle tuning

Vehicle counting

Figure3.2 Architechture of vehicle detection and counting.

3.2.1 Background Registration A general detecting approach is to extract salient regions from the given video clip using a learned background modeling technique. This involves subtracting every image from the background scene. The first frame is assumed as initial background and thresholding the resultant difference image to determine the foreground image. A vehicle is a group of pixels that move in a coherent manner, either as a lighter region over a darker background or vice versa.

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Often the vehicle may be of the same color as the background, or may be some portion of it may be aged with the background, back ground, due to which detecting the vehicle becomes difficult. This leads to an erroneous vehicle count.

3.2.2 Foreground Detection Detecting information can use to refine the vehicle type and also to correct errors which are caused due to occlusions. After registering the static vehicles the background image is subtracted from the video frames to obtain the foreground dynamic vehicles. Post processing is performed on the foreground dynamic vehicles to reduce the noise interference.

3.2.3 Image Segmentation Segmentation Image segmentation steps as follows: 

The segmentation of vehicle regions of interest. In this step, regions which may contain unknown object have to be detected.

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Next step focuses on the extraction of suitable features and then extraction of vehicles. The main purpose of feature extraction is to reduce data by means of measuring certain features that distinguish the input patterns.



The final is classification. It assigns a label to an vehicle based on the information provided by its descriptors. The investigation is made on the mathematical morphology operators for segmentation of a gray-scale image.

3.2.4 Vehicle Tuning The irregular vehicle motion, there always exist some noise regions both in the vehicle and background region .Moreover, the vehicle boundaries are also not very smooth, hence a post processing technique is applied a pplied on the foreground image. Filters termed median filters are used, whose response is based on ordering (ranking) the pixels contained in the image area encompassed by the filter. The final output of the vehicle tuning phase is a binary image of the vehicle detected is termed as mask1.

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3.2.5 Vehicle Counting The tracked binary image mask1 forms the input image for counting. This image is scanned from top to bottom for detecting the presence of vehicle. Two variables are maintained that is count that keeps track of the number of vehicles and count register countreg, which contains the information of the registered vehicle. When a new vehicle is encountered it is first checked to see whether it is already registered in the buffer, if the vehicle is not registered then it is assumed to be a new vehicle and count is incremented, otherwise it is treated as a part of an already existing vehicle and the presence of the vehicle is neglected. This concept is applied for the entire image and the final count of vehicle is present in variable count. A fairly good accuracy of count is achieved. Sometimes due to occlusions two vehicles are merged together and treated as a single entity.

3.3 Modules Specification Vehicle detection and counting method mainly consist of 4 different modules.

3.3.1 Vehicles Detection Moving vehicle detection is in video analysis. It can be used in many regions such as video surveillance, traffic monitoring and people tracking. there are three common motion segmentation techniques, which are frame difference, entropy mask and optical flow method. Frame difference method has less computational complexity, and it is easy to implement, but generally does a poor job of extracting the complete shapes of certain types of moving vehicles. Adaptive background subtraction uses the current frame and the reference image. Difference between the current frame and the reference frame is above the threshold is considered as moving vehicle. Optical flow method can detect the moving vehicle even when the camera moves, but it needs more mo re time for its computational complexity, and it is very sensitive to the noise. The motion area usually appears quite noisy in real images and optical flow estimation involves only local computation. So the optical flow method cannot detect the exact contour of

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the moving vehicle. From the above estimations, it is clear that there are some shortcomings in the traditional moving vehicle detection methods • Frame difference cannot detect the exact contour of the moving the moving vehicle. • Optical flow method is method is sensitive to the noise.

3.3.2 Vehicle Tracking Vehicle tracking involves continuously identifying the detected vehicle in video sequence and is done by specifically marking the boundary around the detected vehicle. Vehicle tracking is a challenging problem. Difficulties in tracking vehicles can arise due to abrupt vehicle motion, changing appearance patterns of the vehicle and the scene, non rigid vehicle structures, vehicleto-vehicle and vehicle-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the vehicle in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. There are three key steps in video analysis- detection of interesting moving vehicle, tracking of such vehicle from frame to frame, and analysis of vehicle tracks to recognize their behavior. Therefore, the use of vehicle of vehicle tracking is pertinent in the tasks of 

Motion-based recognition, that is, human identification based on gait, automatic vehicle detection.



Automated surveillance, that is, monitoring a scene to detect suspicious activities or Unlikely events.

Video indexing, that is, automatic annotation a nd retrieval of the videos in multimedia Databases. 

Human-computer interaction, that is, gesture recognition, eye gaze tracking for data Input to computers, etc.

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Traffic monitoring, that is, real-time gathering of traffic statistics to direct traffic flow.



Vehicle navigation, that is, video-based path planning and obstacle avoidance capabilities.

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Tracking vehicle can be complex due to 

Noise can occur in the images.

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Complexity in vehicle motion.

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Non-rigid or articulated nature of vehicle may arise.

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Partial and full vehicle occlusions can be happen.

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Complexity in vehicle shapes because different ve hicles have different shapes.



Real-time processing requirements also can create a problem.

Several programmers proposed their own algorithms, some of them areEdge detection is used to identify these changes. An important property of edges is that they are less sensitive to illumination changes compared to color features. Algorithms that track the boundary of the vehicles usually use edges as the representative feature, and this algorithm designed by Bowyer et al in 2001. Optical flow is a dense field of displacement vectors which defines the translation of each pixel in a region. It is computed using the brightness constraint, which assumes brightness constancy of corresponding pixels in consecutive frames, designed by Horn and Schunk in 1981. Texture is a measure of the intensity variation of a surface which quantifies properties such as smoothness and regularity. Compared to color, t exture requires a processing step to generate the descriptors. Based on all these points and available data this project is in automated surveillance category, because of its fully automated working and can be implemented in any situations, automated surveillance like traffic, border, etc. And the proposed a new method for tracking called Matrix scan method .Matrix .Matrix scan method has two steps. Firstly, the output image matrix is scanned horizontally, starting at the first x-direction coordinate value, the pixel values in the corresponding column are summed up. The x-direction coordinate value is incremented by one and total pixel value in the next column is calculated. This process is repeated until the last value in the x-direction is found. As a result, the total pixel values of each column are calculated. Each total value is compared to a certain threshold value in order to determine the x-coordinate where an vehicle starts or ends within the image. Secondly, the horizontal scanning method is repeated vertically, thus calculating the total pixel value in each row, and then apply thresholding to determine the y-coordinate where an Dept. of CS&E

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vehicle starts or ends within the image. The background of the image that contains the vehicle is uniform, as it has already been set to white or black at the end of the first phase.

3.3.3 Vehicle counting Steps to count vehicle 

Traverse the mask1 image to detect a vehicle.



If vehicle encountered then check for registration in countreg.



If the vehicle is not registered then increment count and register the vehicle in countreg labeled with the new count.



Repeat steps 2-4 until traversing not completed.

3.3.4 Color identification Vehicle color identification by using slandered color mod el. In slandered color model Color is identified using intensity of the threshold images. The color spaces can separate chromatic and illumination components, maintaining Slandered model regardless of the brightness can lead to an unstable model especially for very bright or dark vehicles conversion also requires computational resources particularly in large images. The idea of preserving intensity components and saving computational costs lead us back to the RGB space. As the requirement to identify moving shadows, we need to consider a color model that can separate slandered and brightness components.

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3.3.5 Data flow diagram for detection modules Read the input video

Background frames Convert video

Foreground frames

Subtraction

Detect the vehicle Fig 3.1 Data flow diagram for detection of vehicle Above figure 3.1 as shows read video clip as the input and read the video is converted into frames. The converted frame is differences from foreground frame and background frame. Frame differences processing stage executed on the image using image subtraction and the background is eliminated thus maintaining only the foreground vehicle and counting the detected vehicle. Moving vehicle detection is the video analysis can be used in many regions for video surveillance, traffic monitoring and vehicle tracking. The common motion segmentation techniques are frame difference, using adoptive ba ckground substraction method.

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3.3.6 Data flow diagram for tracking modules Detected vehicle

Convertin g into gray scale image

Convert gray scale image into binary

Image segmentation

Tracking vehicle

Figure 3.2 Data flow diagram for tracking vehicles Detect vehicle can converting into grayscale image and grayscale image is converted into binary image. The binary image is segmentation to vehicle regions of interest, after the detection of vehicle regions contain unknown objects have to be detected. The extraction of suitable features is vehicle and then extraction of vehicle tracking. tracking vehicle can arise due to abrupt vehicle motion, changing appearance patterns of both the vehicle and the scene, Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the vehicle in every frame. vehicle identified and select the unidentified pixel on every side of the object and change the color of the pixel.The every multiple moving vehicle present in the frame will be tracked successfully.

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3.3.6 Data flow diagram for counting modules Tracked vehicle

Traverse the image

Check for vehicle

Vehicle counting Figure 3.3 vehicle counting The tracked binary image mask1 forms the input image for counting. The image is scanned from top to bottom for detecting the presence of vehicle. Two variables are maintained that is count that keeps track of the number of vehicles and count register variable countreg, which contains the information of the registered vehicle. When a new vehicle is encountered is checked to see whether it is already registered in the buffer, if the vehicle is not registered then it is assumed to be a new vehicle and count is incremented, otherwise it is treated as a part of an already existing vehicle and the presence of the vehicle is neglected. The concept is applied for the entire image and the final count of vehicle is present in variable count. A fairly good accuracy of count is achieved. Sometimes due to occlusions two vehicles are merged together and treated as a single entity.

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CHAPTER-4

DETAILED DESIGN In detail design the algorithms of each modules which is used in this project and the detail description of each module is explained.

4.1 Algorithm for modules Four major functions are involved in the proposed technique. The first function is to read and divide a video clip into number of frames. Second function is to implement the major procedures like finding frame differences and identifying the background back ground registered image .Next post-processing is performed, and the background is eliminated thus maintaining only the foreground vehicles the last function assists in counting the detected vehicles. The aim of the algorithm is to design an efficient counting system on highways .Given a video clip, the initial problem is segregating it into number of frames. Each frame is then considered as an independent image, which is in RGB format and is converted into Gray scale image. Next the difference between the frames at certain intervals is computed. This interval can be decided based on the motion of moving vehicle in a video sequence. If the vehicle is moving quite fast, then the difference between every successive frame is considered. Im-dilate and Im-errosion the morphological operator used for segmenting the vehicles edges counting with bw fill which shows how many areas are big in size, then particular to car areas we locate the points then above the car areas them counter will be incremented step are again repeat till the end of video sequence. Mathematical morphology is used for analyzing vehicle shape characteristics such as size and connectivity, which are not easily accessed by linear approaches. Morphological operations are used for image segmentation. The advantages of morphological approaches over linear approaches are direct geometric interpretation, simplicity and efficiency in hardware implementation .Basic operation of am orphology-based approach is the translation of a structuring element over the image and the erosion and/or dilation of the image content based on the shape of the structuring element. A morphological operation analyzes and manipulates the structure of an image by marking the locations lo cations where the structuring element fits.

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In mathematical morphology, neighborhoods are, therefore, defined by the structuring element, that is the shape of the structuring element determines the shape of the neighborhood in the image. The fundamental mathematical morphology operations dilation and erosion, based on Minkowski algebra are used (Equation 1)

Dilation

D(A,B)=A Xor B=

……(1) Ủβ€B(A+B)……(1)

In equation (1) is either set A or B can be thought of as an "image‖, A is usually considered as the image and B is called a structuring element. The new intensity value of the center pixel is determined according to the following rules. 

For dilation, if any pixel of the rectangle fits at or under the image intensity profile, the center pixel of the rectangle is given the maximum intensity of the pixel and its two neighbors in the original image; otherwise the pixel is set to zero intensity.



For erosion, if the whole rectangle fits at or under the image intensity profile, the center pixel is given the minimum intensity of the pixel and its two neighbors in the original image, otherwise the pixel is set to zero intensity. In short dilation causes vehicles to dilate or grow in size; erosion causes vehicles to shrink. The amount and the way that they grow or shrink depend upon the choice of the structuring element.

4.1.1 Algorithm to Read Video Step 1: Initialize an array M_ Array [ ] to zeros. Step 2: Declare two global variables m and n which stores the row and column col umn values of video frames respectively. Step 3: to for i = 1 to No_ of_ Frames in steps of 1 with Interval 4 frames • Read each frame of video clip and store it. • Store the video frames into M_ Array [ ]. • Increment variable k which stores the total number of frames M_Array.

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end for Step 4: for i = 1 to k Convert images obtained in step 3 from RGB to gray format and store that in a three- dimensional arrayT [m, n, l]. Initialize array variable to Read video and store two matrix value that is rows and columns of video frame. Read each frame from video clips and then store frames into array array then increment the array position to store the next frame frame this process continues untill untill final frame is read and store in array .Image is converted into RGB to gray image and store in 3 dimensional array wher m and n is the row and column value is given to the particular number.

4.1.2 Algorithm for Background Registration ALGORITHM BGRegist ( ) //Input: M_Array //Output: An Image with Registered Background in bg array //Initialize array [ b] to zeros Step 1: for I =1 to m for j=1 to n for k=1 to l-1 if abs(double(T(i,j,l-k))-double(T(i,j abs(double(T(i,j,l-k))-double(T(i,j,k)))<10 ,k)))<10 b(i,j)=T(i,j,k) end if end for end for end for Step 2: Convert b array values to unsigned integers and store it bin to array called background. Step 3: Fill the hole regions in image background and store bit in bg array Step 4: Show the output images background. The background subtraction method is used to obtain the difference image by subtracting two different frames for this operation initially set first frame as background image then fill the Dept. of CS&E

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hole region of image background by using ImFill function and store into the background array. finally shows the background image. . The involves subtracting every image from the background scene. The first frame is assumed as initial background and thresholding the resultant difference image to determine the foreground image A vehicle is a group of pixels that move in a coherent manner, either as a lighter region over a darker background or vice versa. Often the vehicle may be of the same color as the background, or may be some portion of it may be aged with the background, back ground, due to which detecting the vehicle becomes difficult. This leads to an erroneous vehicle count.

4.1.3 Algorithm for Background Elimination ALGORITHM BGElimin( ) //Input: d is a specific Video Frame //Output: An image with Foreground Vehicles is stored in’c’ //Initialize c array to zeros Step 1: for i = 2 to m-1 for j = 2 to n-1 if the difference between the pixel values of d array with bg or b array is less than 20 store value 0 for that pixel in array c else111 store pixel value end if end for end for Step 2: Convert the values in c array and apply median filter. Step 3: Show the output images c, d The vehicle foreground image is store into the array variable c is initializing to zero .if d is the specific video Frame. Frame. the difference between the pixel values of d array with background image b and array is less than 20 store the value zero for the difference between the pixel in

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array c otherwise store the pixel value. v alue. Convert stored value c array and apply median filter for removing the noise.

4.1.4 Algorithm for Counting ALGORITHM Count ( ) //Initialise count = 0 and counted register buffer countveg = 0 Step 1: Traverse the vehicle image to detect a vehicle from top to bottom. Step 2: If vehicle encountered then check for registration in countveg. Step 3: If the vehicle is not registered then increment count and register the vehicle in countveg, labelled with the new count. Step 4: Repeat steps 2-4 until traversing not completed. The tracked binary image mask1 forms the input image for counting. This image is scanned from top to bottom for detecting the presence of vehicle. Two variables are maintained that is count that keeps track of the number of vehicles and count cou nt register countreg, which contains the information of the registered vehicle. When a new vehicle is encountered is checked to see whether it is already registered in the buffer, if the vehicle is not registered then it is assumed to be a new vehicle and count is incremented, increme nted, otherwise it is treated as a part of an already existing vehicle and the presence of the vehicle is neglected. The concept is applied for the entire image and the final count of vehicle is present in variable count. A fairly good accuracy of count is achieved. Sometimes due to occlusions two vehicles are merged together and treated as a single entity.

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CHAPTER-5 IMPLEMENTATION IMPLEMENTATION REQUIREMENTS REQUIREMENTS The implementation of the vehicle detection and counting on traffic video by using image processing with simulation software is as follows

5.1 Simulation Software Simulation is performed using MATLAB Software. This is an interactive system whose basic data element is an array that does not require dimensioning. It is a tool used for formulating solutions to many technical computing problems, especially those involving matrix representation. This tool emphasizes a lot of importance on comprehensive prototyping environment in the solution of digital image processing. Vision is most advanced of our senses, hence images images play an important role in humans’ perception, and MATLAB is a very efficient tool for image processing.

5.2 Main Pseudo code function varargout = MAINCODE(varargin) MAINCODE MATLAB code for MAINCODE.fig MAINCODE, by itself, creates a new MAINCODE or raises the ex isting H = MAINCODE returns the handle to a new MAINCODE or the handle to gui_Singleton = 1; gui_State = struct('gui_Name',

mfilename, ...

'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @MAINCODE_OpeningFcn, ... 'gui_OutputFcn', @MAINCODE_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end

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if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end function MAINCODE_OpeningFcn(hObject, eventdata, handles, varargin)

handles.output = hObject; a = ones(256,256); axes(handles.axes1); imshow(a); axes(handles.axes2); imshow(a); axes(handles.axes3); imshow(a); guidata(hObject, handles); function varargout = MAINCODE_OutputFcn(hObject, eventdata, handles) varargout{1} = handles.output; --- Executes on button press in pushbutton3. function pushbutton3_Callback(hObject, eventdata, handles) hObject

handle to pushbutton3 (see GCBO)

eventdata reserved - to be defined in a future version of MATLAB handles

structure with handles and user data (see GUIDATA)

close all;

--- Executes on button press in pushbutton4. function pushbutton4_Callback(hObject, eventdata, handles) hObject

handle to pushbutton4 (see GCBO)

eventdata reserved - to be defined in a future version of MATLAB handles

structure with handles and user data (see GUIDATA)

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a = ones(256,256); axes(handles.axes1); imshow(a); axes(handles.axes2); imshow(a); axes(handles.axes3),imshow(a); set(handles.text12,'string',''); set(handles.text13,'string',''); set(handles.text14,'string',''); set(handles.edit1,'string',''); Graphical Objects in MATLAB are arranged in a structure called the Graphics Object Hierarchy which can be viewed as a rooted tree with the nodes representing the objects and the root vertex corresponding to the root object. The form of such a tree depends on the particular set of objects initialised and the precedence relationship between its nodes is reflected in the object’s properties Parent and Children. If for example, the handle h of object A is the value of the property Parent P arent of object B, then A is the parent of B, and, accordingly, B is a child of o f A. The possibility to have one or another form of hierarchy depends on the admissible values of objects’ properties Parent and/or Children. One also has to be aware of some specific rules which apply to the use of particular objects. For example, it is recommended that do not parent any objects to the annotation axes, and do not change explicitly annotation axes’ properties Similarly, parenting annotation objects to standard axes may cause errors enter the code.

5.2.2 Pseudo code for background subtraction function pushbutton2_Callback(hObject, eventdata, handles) diry2=handles.diry; videoReader = vision.VideoFileReader(diry2);%visiontraffic.avi blobAnalysis = vision.BlobAnalysis('BoundingBoxOutputPort', true, ... 'AreaOutputPort', false, 'CentroidOutputPort', false, ... 'MinimumBlobArea', 150); fontSize = 14; Dept. of CS&E

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i=1; alpha = 0.5; count=0; while ~isDone(videoReader) thisFrame = step(videoReader);

if i == 1 Background = thisFrame; else Change background slightly at each frame Background(t+1)=(1-alpha)*I+alpha*Background Background = (1-alpha)* thisFrame + alpha * Background; end Display the changing/adapting background. subplot(1, 3, 1); axes(handles.axes1),imshow(Background); title('Adaptive Background'); Calculate a difference between this frame and the background. differenceImage = thisFrame - Background; Threshold with Otsu method. grayImage = rgb2gray(differenceImage); Convert to gray gray level thresholdLevel = graythresh(grayImage); Get threshold. threshold. binaryImage = im2bw( grayImage, thresholdLevel); Do the binarization se = strel('square', 5); binaryImage=imdilate(binaryImage,se);

binaryImage=imdilate(binaryImage,se);

binaryImage=imopen(binaryImage,se); binaryImage=imclose(binaryImage,se); [binaryImage,IDX] = imfill(binaryImage,'holes'); [binaryImage,IDX] = imfill(binaryImage,'holes'); [binaryImage,IDX] = imfill(binaryImage,'holes'); blobAnalysis = vision.BlobAnalysis('BoundingBoxOutputPort', true, ... Dept. of CS&E

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'AreaOutputPort', false, 'CentroidOutputPort', false, ... 'MinimumBlobArea', 150); bbox = step(blobAnalysis, binaryImage);

result = insertShape(thisFrame, 'Rectangle', bbox, 'Color', 'green'); labeledImage = bwlabel(binaryImage, 8); if count==0 count=contv(binaryImage,grayImage); cres=count; else [cres,count]=cont_up(count,cres,binaryImage,grayImage); end numCars = size(bbox, 1); if numCars>=1 [ colour ,loc] = colour_iden(thisFrame,binaryImage); colour=cell2mat(colour); set(handles.text12,'string',text); [ colour ,loc] = colour_iden(thisFrame,binaryImage); result = insertText(result, [loc(2,h) loc(1,h)], num2str(h), 'BoxOpacity', 1,'FontSize', 8); set(handles.text13,'string','VEICLE COUNT'); set(handles.text14,'string',num2str(count)); result = insertText(result, [1 1], count, 'BoxOpacity', 1,'FontSize', 8); Plot the binary image. subplot(1, 3, 2); axes(handles.axes2),imshow(binaryImage); title('Binarized Difference Image'); subplot(1, 3, 3); axes(handles.axes3),imshow(result); title('Tracking of veicle'); i=i+1; Dept. of CS&E

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for g=1: set(handles.edit2,'string','1.red 2.blue 3.rdfdd '); pause(0.1); end The preprocessing, background substraction is foreground detection, and data validation. Preprocessing consists of a collection of simple image processing tasks that change the raw input video into a format that can be processed by subsequent. Background modeling uses the new video frame to calculate and update A background model provides a statistical description of the entire background scene. Foreground detection then identi_es pixels in the video frame that cannot be adequately explained by the background model, and outputs them as a binary candidate foreground mask. Finally, data validation examines the candidate mask, eliminates those pixels that do not correspond to actual moving vehicles, and outputs the foreground mask. The Domain knowledge and computationally-intensive vision algorithms are often used in data validation. Real-time processing is still feasible as these sophisticated algorithms are applied only on the small number of candidate foreground pixels. .

5.3 Pseudo code for detection and tracking videoReader = vision.VideoFileReader(diry2);%visiontraffic.avi blobAnalysis = vision.BlobAnalysis('BoundingBoxOutputPort', true, ... 'AreaOutputPort', false, 'CentroidOutputPort', false, ... 'MinimumBlobArea', 150); fontSize = 14; i=1; alpha = 0.5; count=0; while ~isDone(videoReader) thisFrame = step(videoReader);

if i == 1 Background = thisFrame; else Dept. of CS&E

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Change background slightly at each frame Background(t+1)=(1-alpha)*I+alpha*Background Background = (1-alpha)* thisFrame + alpha * Background; end Display the changing/adapting background. subplot(1, 3, 1); axes(handles.axes1),imshow(Background); title('Adaptive Background'); Calculate a difference between this frame and the background. differenceImage = thisFrame - Background; Threshold with Otsu method. grayImage = rgb2gray(differenceImage); Convert to gray gray level thresholdLevel = graythresh(grayImage); Get threshold. threshold. binaryImage = im2bw( grayImage, thresholdLevel); Do the binarization se = strel('square', 5); binaryImage=imdilate(binaryImage,se);

binaryImage=imdilate(binaryImage,se);

binaryImage=imopen(binaryImage,se); binaryImage=imclose(binaryImage,se); [binaryImage,IDX] = imfill(binaryImage,'holes'); [binaryImage,IDX] = imfill(binaryImage,'holes'); [binaryImage,IDX] = imfill(binaryImage,'holes'); blobAnalysis = vision.BlobAnalysis('BoundingBoxOutputPort', true, ... 'AreaOutputPort', false, 'CentroidOutputPort', false, ... 'MinimumBlobArea', 150); bbox = step(blobAnalysis, binaryImage); result = insertShape(thisFrame, 'Rectangle', bbox, 'Color', 'green'); Detecting changes in image sequences of the same scene, captured at different times, is of significant interest due to a large number of applications in several disciplines. Video surveillance is among the important applications, which require reliable detection of changes in the scene. There are several different approaches for such a detection problem. These methods Dept. of CS&E

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can be separated into two conventional classes: temporal differencing and background modeling and subtraction . The former approach is possibly the simplest one, also capable of adapting to changes in the scene with a lower computational load. However, the detection performance of temporal differencing is usually quite poor in real-life surveillance applications. On the other hand, background modeling and subtraction approach has been used successfully in several algorithms Background subtraction is easy. If you want to subtract a constant value, or a background with the same size as your image, you simply write img background. imsubtract simply

=

img

-

makes sure that the output is zero wherever the background is

larger than the image. Background estimation is hard. There you need to know what kind of image you're looking at, otherwise, the background b ackground estimation will fail. Tracking moving vehicles in video streams has been an area of research in computer vision. In a real time system for measuring traffic parameters is described. Tracking uses a feature-based method along with occlusion reasoning for tracking vehicles in congested traffic scenes. In order to handle occlusions, instead of tracking entire vehicles, vehicle sub features are tracked. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the vehicle in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. There are three key steps in video analysis: detection of interesting moving vehicle, tracking of vehicle from frame to frame, and analysis of vehicle tracks to recognize their behavior. Otsu's method is an image processing technique that can be used to convert a greyscale image into a purely binary image by calculating a threshold to split pixels into frames.

5.4 Pseudo code for counting function [cres,count]=cont_up(count,cres,binaryImage,grayImage) labeledImage = bwlabel(binaryImage, 8); stats = regionprops(labeledImage,'BoundingBox','Area'); area = cat(1,stats.Area); if length(area)>1 [~,maxAreaIdx] = max(area); bb = round(stats(maxAreaIdx).BoundingBox); Dept. of CS&E

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a=bb(2)+bb(4); b=bb(1)+bb(3); s=size(labeledImage,1); if a<=s && b<=s croppedImage = grayImage(bb(2):bb(2)+bb(4),bb(1):bb(1)+bb(3),:); croppedImage1 = binaryImage(bb(2):bb(2)+bb(4),bb(1):bb(1) binar yImage(bb(2):bb(2)+bb(4),bb(1):bb(1)+bb(3),:); +bb(3),:); else a=a-(a-s); b=b-(b-s); croppedImage = grayImage(bb(2):a,bb(1):b,:); croppedImage1 = binaryImage(bb(2):a,bb(1):b,:); end croppedImage=imresize(croppedImage,[50,50]); cd database imwrite(croppedImage,strcat(num2str(1),'.jpg')); cd .. end area=area(area>150); [~,maxAreaIdx] = max(area); if length(area)cres a=countnew(area,grayImage,stats); count1=length(area)-cres; count=count+count1; cres=length(area); else if length(area)==count count=count; cres=cres; Dept. of CS&E

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end end end end function out=countnew(area,grayImage,stats) out=0; for i=1:length(area) bb = round(stats(i).BoundingBox); note that regionprops switches x and y (it's a long story) a=bb(2)+bb(4); b=bb(1)+bb(3); s=size(grayImage,1); if a<=s && b<=s croppedImage = grayImage(bb(2):bb(2)+bb(4),bb(1):bb(1)+bb(3),:); croppedImage1 = binaryImage(bb(2):bb(2)+bb(4),bb(1):bb(1) binar yImage(bb(2):bb(2)+bb(4),bb(1):bb(1)+bb(3),:); +bb(3),:); else a=a-(a-s); b=b-(b-s); croppedImage = grayImage(bb(2):a,bb(1):b,:); croppedImage1 = binaryImage(bb(2):a,bb(1):b,:); end croppedImage=imresize(croppedImage,[50,50]); cd database b1=imread('1.jpg'); cd .. a1=corr2(croppedImage,b1); if a1>=0.45 out=out+1; end end Dept. of CS&E

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end The tracked binary image mask1 forms the input image for counting. Binary image is scanned from top to bottom for detecting the presence of vehicle. Two variables are maintained that is count that keeps track of the number of vehicles and count register countreg, which contains the information of the registered vehicle. When a new vehicle is encountered it is first checked to see whether it is already registered in the buffer, if the vehicle is not registered then it is assumed to be a new vehicle and count is incremented, otherwise it is treated as a part of an already existing vehicle and the presence of the vehicle is neglected. the Blob Analysis block to calculate statistics for labeled regions in a binary image. The block returns quantities such as the centroid, bounding box, label matrix, and blob count. The Blob Analysis block supports input and output counting vehicle.

5.5 Pseudo code for color identification function [ c,loc ] = colour_iden( originalImage,binaryImage) labeledImage = bwlabel(binaryImage, 8); stats = regionprops(labeledImage,'BoundingBox','Area'); area = cat(1,stats.Area); i1=1; for i=1:length(area) if area(i)>150 bb = round(stats(i).BoundingBox); a=bb(2)+bb(4); b=bb(1)+bb(3); s=size(labeledImage,1); s1=size(labeledImage,2); if a
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a=a-(a-s); b=b-(b-s); for j=1:3 croppedImage(:,:,j) = originalImage(bb(2):a,bb(1):b,j); end end loc(1,i)=bb(2); loc(2,i)=bb(1); croppedImage=croppedImage*255; r=mean2(croppedImage(:,:,1)); g=mean2(croppedImage(:,:,2)); b=mean2(croppedImage(:,:,3)); c(i1)=colur1(r,g,b); i1=i1+1; end clear croppedImage; end end

function c=colur1(r,g,b) chipNames

=

{'DarkSkin';'LightSkin';'BlueSky';'Foliage';'BlueFlower';'Bluish

Green';'Orange';'PurpleRed';ModerateRed';'Purple';'YellowGreen';'OrangeYellow';'Blue';'Green';' White';'Yellow';'Magenta';'Cyan';'Red';'Neutral 8';'Neutral 65';'Neutral 5';'Neutral 35';'Black'};

sRGB_Values = ([...115,82,68194,150,13098,122,15787,108,67133,128,177103,189,170 214,126,4480,91,166193,90,9994,60,108157,188,64224,163,4656,61,15070,148,73175,54, 60,199,31187,86,1498,133,161243,243,242200,200,200160,160,160122,122,12185,85,85 52,52,52]); r1=abs(sRGB_Values(:,1)-r); g1=abs(sRGB_Values(:,1)-g); Dept. of CS&E

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b1=abs(sRGB_Values(:,1)-b); a=[r1,g1,b1]'; [d,f]=min(sum(a)); c=chipNames(f); end many color spaces can separate chromatic and illumination components, maintaining Slandered model regardless of the brightness can lead to an unstable model especially for very bright or dark vehicles conversion also requires computational resources particularly in large images. The idea of preserving intensity components and saving computational costs lead us back to the RGB space. sp ace. As the requirement to identify moving shadows, we need to consider a color model that can separate slandered and brightness components. It should be compatible and make use of our mixture model.

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CHAPTER-6

TESTING The actual purpose of testing is to discover errors. Testing is the process of trying to discover every conceivable fault or weakness in a work product. It is the process of exercising software with the intent of ensuring that the Software system meets its requirements and user expectations and does not fail in an unacceptable manner.

TYPES OF TESTING There are many types of testing methods are available in that mainly used testing methods are as follows

6.1.1 Unit Testing Unit testing involves the design of test cases that validate that the internal program logic is functioning properly, and that program produces valid outputs. All decision branches and internal code flow should be validated. It is the testing of individual software units of the application. It is done after the completion o f individual unit before integration

6.1.2 Integration Testing Software integration testing is the incremental integration testing of two or more integrated software components on a single platform to produce failures caused by interface defects. The task of the integration test is to check that components or software applications e.g components in the software system or one step up software applications at the company level interact without error.

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6.2 Detection Phase Of Moving cars Test Case

Input Frame

Expected Result

Output Frame

Remark

Test passed

1

Moving vehicle in Clausius image

2

Test passed

Test passed 3

Table 6.2: Testing for the Vehicle Detection o f a moving cars. The above tables 6.2 give different test cases done on the videos at different frame location to detect the vehicle. In the moving cars video, the frame sequences used are 70, 168 and 232 respectively.

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6.3 Tracking Phase of Moving cars Test Case

Input Frame

Expected Result

Output Frame

Remark

Test passed

1

Moving vehicle tracking.

2

Test passed

Test passed

3

Table 6.3: Testing for Vehicle Tracking by Matrix Scan of a moving cars. The above tables 6.3 give different test cases done on the videos at different frame in tracking multiple moving vehicle. In the moving cars video, the frame sequences used are 70, 168 and 232 respectively.

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6.3 Counting Phase of Moving cars Test Case

Input Frame

Expected Result

Output Frame

Remark

Test passed

1

Moving vehicle tracking.

2

Test passed

Test passed

3

Table 6.3: Testing for Vehicle counting of moving cars. The above f igure igure 6.3 shows test case for vehicle counting the test case 1 is input frame is 1 and out frame is counting the number of vehicle is 1 then test case is passed. second test case and third test case also passed. . The concept is applied for the entire image and the final count of vehicle is present in variable count. A fairly good accuracy of count is achieved. Sometimes due to occlusions two vehicles are merged together to gether and treated as a single entity.

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CHAPTER -7 RESULT

Figure 7.1 Graphical user interfaces Graphical user interface is the browses, select the required video from the directory and also play the videos. The Graphical user interface is used clear and close all videos. The following vehicle are grouped under the category of MATLAB User Interface Interface Objects are UI controls check boxes, earditable text t ext fields, list boxes, b oxes, pop-up menus, push buttons, radio buttons, sliders, static text boxes, toggle buttons. UI toolbar (uitoolbar), can parent object of type uipushtool and uitoggletool UI context menus and UI menus vehicle of type uicontextmenu and uimenu Container vehicle uipanel and uibuttongroup.

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Figure 7.1 snapshot of detection and tracking of vehicles By using the detection and counting methods, various results are obtained. Video sequence taken containing moving cars and walking person. These videos are processed to get a detected and extracted object. Following snapshots shows the results obtained in each step of the process. The output of the the segmentation is a binary vehicle mask perform region extraction on mask. In In the region tracking want to associate regions in frame i+1 with the regions in frame . The allows us to compute the velocity of the region as it moves across the image and also helps in the vehicle tracking stage. There are certain problems that need to be handled for reliable and robust region tracking. Dept. of CS&E

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Figure 7.2 Snapshot of vehicles counting. Figure 7.2 shows the count of total tracked vehicles that are passed still this given frame. Initially count register is set to zero ,when an y moving vehicles are tracked than count register is incremented. When a new vehicle is encountered is checked to see whether it is already registered in the buffer, if the vehicle is not registered then it is assumed to be a new vehicle and count is incremented, otherwise it is treated as a part of an already existing vehicle and the presence of the vehicle is neglected.

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Figure 7.3 moving vehicles counting and color identification. Each Moving vehicles color is identified and displayed as shown in the figure 7.3, color of vehicles in the given video is determined by using slandered color model. Color spaces can separate chromatic and illumination components, maintaining Slandered model regardless of the brightness can lead to an unstable model especially for very bright or dark vehicles conversion also requires computational resources particularly in large images. The ide a of preserving intensity components and saving computational costs lead us back to the RGB space.

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CHAPTER-7

CONCLUSION A system has been developed to detect and count dynamic vehicles on highways efficiently. The system effectively combines simple domain knowledge about vehicle classes with time domain statistical measures to identify target vehicles in the presence of partial occlusions and ambiguous poses, and the background clutter is effectively rejected. The experimental results show that the accuracy of counting vehicles was 96%, although the vehicle detection was 100% which is attributed towards partial occlusions. The computational complexity of our algorithm is linear in the size of a video frame and the number of vehicles detected. As we have considered traffic on highways there is no question of shadow of any cast such as trees but sometimes due to occlusions two vehicles are merged together and treated as a single entity.

Future Scope Several future enhancements can be made to the system. The detection and tracking and counting of moving vehicle can be extended to real-time live video feeds. Apart from the detection and extraction, process of recognition can also be done. By using recognition techniques, the vehicle in question can be classified. Recognition techniques would require an additional database to match with the given vehicle.The system is designed for the detection and tracking and counting of a multiple multiple moving vehicle. It can be further devised to alarming system.

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REFERENCES [1] P.M.Daigavane and Dr. P.R.Bajaj , Real Time Vehicle Detection and Counting Method for Unsupervised Traffic Video on Highways Mrs. [2] Chen S. C., Shyu M. L. and Zhang C., ―An Intelligent Framework for Spatio-Temporal Spatio-Temporal th

Vehicle Vehicle Tracking‖, 4 International IEEE Conference on Intelligent Transportation Systems, Oakland,California, USA, Aug. 2001. [3] Gupte S., Masoud O., Martin R. F. K. and Papanikolopoulos N. P.,―Detection P.,―Detection and Classification of Vehicles‖, In IEEE Transactions on Intelligent Transportation Systems, vol. 3, no. 1, March, 2002, pp. 37 – 47. 47. [4] Dailey D. D. J., Cathey F. and Pumrin S., ―An Algorithm to Estimate Mean Traffic Speed Using Uncalibrated Cameras‖, In IEEE Transactions on Intelligent Ttransportations Systems, vol. 1, no. 2,pp. 98-107, June, 2000. [5] S. Cheung and C. Kamath, ―Ro[1] ―Ro[1] Chen S. C., Shyu Shyu M. L. and Zhang C., ―An Unsupervised Segmentation Framework for Texture Image Queries‖, In the 25th IEEE Computer Society International Computer Software and Applications Conference (COMPSAC), Chicago, Illinois, USA, Oct.2000. [6] N. Kanhere, S. Pundlik and S. Birchfield, ―Vehicle Segmentation and Tracking Tracking from a LowAngle Off-Axis Camera‖, In IEEE Conference on Computer Vision and Pattern Recognition‖, San Diego, June,2005. [7] Deva R., David A., Forsyth and Andrew Z., ― Tracking People by Lear ning ning their Appearance‖, In IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. 29, no1, Jan. 2007. [8] Toufiq P., Ahmed Egammal and Anurag Mittal, ―A Framework for Feature Feature Selection for Background Subtraction‖, In Proceedings of IEEE Computer Society Society Conference on Computer Vision and Pattern Recognition(CVPR’06), Recognition(CVPR’06), 2006. [9] P. Kaewtra Kulpong and R. Bowden, ―An Improved Adaptive Background Mixture Model for Real-time Real-time Tracking with Shadow Detection‖, In Proceedings of the 2nd European Workshop on Advanced Video-Based Surveillance Systems, Sept. 2001.

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APPENDICES Functions Functions differ from scripts. They take explicit input and output arguments. As all the other matlab commands, a function can be called within a script or from the command line.

Functions used There are many simulation functions are there some of them which are used in this project are as follows

1. mmreader class Syntax of mmreader function as fallows. obj = mmreader (filename) obj = mmreader (filename, 'PropertyName', PropertyValue) Description

The mmreader function is used to Create multimedia reader vehicle for reading video files.Use mmreader with the read method to read video data from a multimedia file into the MATLAB workspace.

2. read Syntax of read as fallows video = read(obj) video = read(obj, index) Description

video = read(obj) reads in all video frames from the file associated with obj.

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video = read(obj, index) reads only the specified frames. Index can be a single number or a twoelement array representing an index range of the video stream

3. size Systax of Size function as fallows d = size(X) [m,n] = size(X) m = size(X,dim) [d1,d2,d3,...,dn] = size(X), Description

The size is used to Array dimensions where d = size(X) returns the sizes of each dimension of array X in a vector d with ndims(X) elements. If X is a scalar, which MATLAB software regards as a 1-by-1 array, size(X) returns the vector [11].[m,n] = size(X) returns the size of matrix X in separate variables m and n.m = size(X,dim) returns the size of the dimension of X specified by scalar dim. [d1,d2,d3,...,dn] = size(X), for n > 1, returns the sizes of the dimensions of the array X in the variables d1,d2,d3,...,dn, provided the number of output arguments n equals ndims(X). If n does not equal ndims(X), the following exceptions hold:

4. implay Syntax of implays function as mplay implay( filename) implay(I) implay(…, implay(…, FPS) FPS) Dept. of CS&E

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Description

Implay function is is used Play movies, videos, videos, or image sequences. sequences. mplay opens a Movie Player for showing MATLAB movies, videos, or image sequences (also called image stacks). Use the implay File menu to select the movie or image sequence that you want to play. Use implay toolbar buttons or menu options to play the movie, jump to a specific frame in the sequence, change the frame rate of the display, or perform other exploration activities. One can open multiple implay impla y movie players to view different movies simultaneously. implay(filename) opens the implay movie player, displaying the content of the file specified by filename. The file can be an Audio Video Interleaved (AVI) file. implay reads one frame at a time, conserving memory during playback. implay does not play audio tracks. implay(I) opens the implay movie player, displaying the first frame in the multiframe image array specified by I. I can be a MATLAB movie structure, or a sequence of binary, grayscale, or truecolor images. A binary or grayscale image sequence can be an M-by-N-by-1 by-K array or an M-by-N-by-K M -by-N-by-K array. A true color col or image sequence must be an M-by-N-by-3-byK array. implay(..., FPS) specifies the rate at which you want to view the movie or image sequence. The frame rate is specified as frames-per-second. If omitted, implay uses the frame rate specified in the file or the default value 20.

5. regionprops Syntax of regionprops function as fallows STATS = regionprops(BW, properties) STATS = regionprops(CC, properties) STATS = regionprops(L, properties) STATS = regionprops(..., I, properties) Description

The regionnprop is Measure properties of image regions Dept. of CS&E

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STATS = regionprops(BW, properties) measures a set of properties for each connected component (vehicle) in the binary image, BW. The image BW is a logical array; it can have any dimension. STATS = regionprops(CC, properties) measures a set of properties for each connected component (vehicle) in CC, which is a structure returned by bwconncomp. STATS = regionprops(L, properties) measures a set of properties for each labeled region in the label matrix L. Positive integer elements of L correspond to different regions. For example, the set of elements of L equal to 1 corresponds to region 1; the set of elements of L equal to 2 corresponds to region 2; and so on. STATS = regionprops(..., I, properties) measures a set of properties for each labeled region in the image I. The first input to regionprops — either either BW, CC, or L — identifies identifies the regions in I. The sizes must match: size(I) must equal size(BW), CC.ImageSize, or size(L). STATS is a structure array with length equal to the number of vehicles in BW, CC.NumVehicles, or max(L(:)). The fields of the structure array denote different properties for each region, as specified by properties. Properties

Properties can be a comma-separated list of strings, a cell array containing strings, the single string 'all', or the string 'basic'. If properties are the string 'all', regionprops computes all the shape measurements, listed in Shape Measurements. If called with a grayscale image, regionprops also returns the pixel value measurements, listed in Pixel Value Measurements. If properties are not specified or if it is the string 'basic', regionprops computes only the 'Area', 'Centroid', and 'BoundingBox' measurements. One can calculate the following properties on N-D inputs: 'Area', 'BoundingBox', 'Centroid', 'FilledArea', 'FilledImage', 'Image', 'PixelIdxList', 'PixelList', and 'SubarrayIdx'

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6. floor Syntax of floor function as fallows B = floor(A) Description

The floor function Round toward negative infinity.B = floor(A) rounds the elements elements of A to the nearest integers less than or equal to A. For complex A, the imaginary and real parts are rounded independently.

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Vehicle_detection_and_counting_in_traffi.pdf

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