Research Article 2017

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1 International Journal of Emerging Research in Management &Technology ISSN: (Volume-6, Issue-5) Research Article May 2017 Special Issue of International Conference on Emerging Trends in Science & Engineering (ICETSE 2017) Conference Held by IEAE India, at Coorg Institute of Technology, Ponnampet, Karnataka, India Comparative Study of Edge Detection Technique in Video Streams using Simulink and XSG Blockset Sunitha Lasrado Assistant Professor Dept. of ECE, NMAMIT, Nitte Karnataka, India Shahla Parveen PG student Dept. of ECE NMAMIT, Nitte Karnataka, India Abstract: In metropolitan areas, due to increase in number of vehicles year after year, traffic has become a major concern throughout the world. To control the increasing traffic congestion there is a need to control the traffic density which can be performed by detecting the edges of the vehicles in traffic lane. In this paper a traffic surveillance system is designed to perform surveillance task. The main objective is to detect edges of the vehicles using image processing techniques. These techniques are designed in MATLAB environment using Simulink and Xilinx System Generator (XSG) blockset. Comparative studies on performance metrics for different edge detection algorithms are analyzed. The video frames are at a rate of 15fps. The vehicle edge detection also takes place at the same frame rate per second. Keywords Edge detection; Video processing; MATLAB; XSG I. INTRODUCTION Video processing is used to detect, enhance, analysis, compose and restore a frame of a video. It improves the quality of each frame. Operations like video segmentation using edge detection help us to analyze various applications. It focuses on processing a video, frame by frame. Edge detection plays a vital role in video processing application. Detected edges are important in segmentation, scene analysis, area selection and object recognition etc. In [1] intelligent transportation system for smart cameras is designed for vehicle density using different image processing technique. In [2-4] proposed Simulink model to detect and track moving vehicles using background subtraction, optical flow, edge detection and shadow detection method. In [5] developed a real time traffic signal control model to compute traffic load using Simulink blockset. In [6-7] Sobel and Prewitt algorithms are implemented over Field Programmable Gate Array. In [8] color space conversion model for video processing is designed using XSG blockset. In [9-10] presented implementation of an image processing algorithm applicable for Sobel edge detection of an image in Xilinx Spartan 3 FPGA by using System Generator. In [11] Simulink and XSG model is designed to read and enhance images using image processing technique. The aim of this paper is to detect edges of the moving vehicle in a video by using Simulink and XSG blockset from MATLAB environment. Section II gives basics of edge detection algorithm. Section III includes methodology. Section IV describes results and discussions followed by the references. II. EDGE DETECTION TECHNIQUE It is a mathematical method to identify points in a frame of video at which each frame brightness changes formally and has cut offs. The point at which each frame brightness changes formally are structured into set of curve termed as edges. In the area of feature extraction and detection it is a necessary technique in video and image processing. In this technique, each frame of a video is convolved with coefficient in x and y direction. Mathematically expressed as follows (1) Mathematically gradient can be computed by equation (2) These techniques are classified as first order derivative and Second order derivative. In this paper first order derivative are used which are detailed as followed: A. Sobel edge detection It is based on convolving the frame of a video with small, separable and integer valued filter in the horizontal and vertical directions. Absolute magnitude is obtained by adding horizontal and vertical mask. Masks are designed mathematically given by equation (3). ( ) ( ) ( ) ( ) (3) All Rights Reserved Page 265

Where = gradient operator = coefficient of gradient operator. It uses two 3*3 kernels which are convolved with each frame of a video to calculate derivatives. B.

Masks are designed by calculating gradient values which are mathematically given by equation (4). ( ) ( ) ( ) ( ) (4) Where gradient operator = coefficient of gradient operator.

The edges are highlighted by regions of high spatial frequency. The algorithm is a 2*2 convolution mask which is given below III. METHODOLOGY A.

2 Where = gradient operator = coefficient of gradient operator. It uses two 3*3 kernels which are convolved with each frame of a video to calculate derivatives. B. Prewitt edge detection It is based on convolving the frame of a video with small, separable and integer valued filter in the horizontal and vertical directions like Sobel. Masks are designed by calculating gradient values which are mathematically given by equation (4). ( ) ( ) ( ) ( ) (4) Where gradient operator = coefficient of gradient operator. The 3*3 convolution mask is given as, C. Robert edge detection It performs simple, fast computation, 2-D spatial gradient measurement of a video. The edges are highlighted by regions of high spatial frequency. The algorithm is a 2*2 convolution mask which is given below III. METHODOLOGY A. Edge detection using Simulink blockset Figure 1 shows the flow of edge detection technique by using Simulink blockset. The steps are detailed as followed: The input video is taken from MATLAB workspace. The input color video is converted into gray scale video. Edges are detected by using edge detection block by using Sobel, Prewitt and Robert technique. The edge detected output is displayed in video viewer. B. Edge detection using XSG blockset Figure 1: Flow of Edge detection technique using Simulink Blockset Figure 2: Flow of Edge detection technique using XSG Blockset All Rights Reserved Page 266

3 Figure 3: Frame to serial conversion block diagram Figure 2 shows the flow of edge detection technique by using XSG blockset. The steps are detailed as followed: The input video is taken from MATLAB workspace. The processing is done on input video which basically includes conversion from frame to serial. For XSG implementation, each video frame must be transformed into 1D data array. Figure 3 shows the frame to serial conversion block diagram. The object edge detection algorithm is designed for Sobel, Prewitt and Robert technique using XSG block sets. Figure 4 shows the edge detection block diagram using XSG. Serial to frame conversion is performed on the edge detected output. Figure 5 shows the Serial to frame conversion block diagram. This block is required to recreate 1-D array into 2-D frame. The output video is showed using video viewer which provides edge detected result of input video. Figure 4: Edge detection block diagram using XSG Figure 5: Serial to frame conversion block diagram IV. RESULTS AND DISCUSSIONS The edge technique designed for Sobel, Prewitt and Robert was tested with a video of 120*160 sizes. The video frames are at a rate of 15fps. The system was implanted by using Simulink and XSG block set from MATLAB environment. It is observed that from TABLE 1 Robert edge detection performs efficiently for 1 simulation time using Simulink blockset and from TABLE 2 Sobel edge detection performs efficiently for 1 simulation time using XSG blockset. Figure 6 and Figure 7 show the plot of time execution speed for different edge detection technique using Simulink and XSG blockset. Table 1: Performance metrics for different techniques using Simulink blockset Sobel Prewitt Robert Total recorded time 3.63s 4.13s 3.01s Block methods Internal methods All Rights Reserved Page 267

9 9 9 Model methods Clock precision 0.00000005s 0.

00000005s Clock speed 2100MHz 2100MHz 2100MHz Figure 6: Plot of time execution speed for different edge detection

Table 2: Performance metrics for different techniques using XSG blockset Sobel Prewitt Robert Total recorded 137.

4 9 9 9 Model methods Clock precision s s s Clock speed 2100MHz 2100MHz 2100MHz Figure 6: Plot of time execution speed for different edge detection technique using Simulink blockset. Table 2: Performance metrics for different techniques using XSG blockset Sobel Prewitt Robert Total recorded s s s time Block methods Internal methods Model methods Clock s s s precision Clock speed 2100MHz 2100MHz 2100MHz Figure 7: Plot of time execution speed for different edge detection technique using XSG blockset (a) (b) (c) (d) Figure 8: Edge detection using Simulink Blockset All Rights Reserved Page 268

5 Figure 8 shows the edge detection using Simulink blockset. Figure (a) shows the original video frame, (b) shows the Sobel edge detection, (c) shows the Prewitt edge detection and (d) shows the Robert edge detection. (a) (b) (c) (d) Figure 9: Edge detection using XSG Blockset Figure 9 shows the edge detection using XSG blockset. Figure (a) shows the original video frame, (b) shows the Sobel edge detection, (c) shows the Prewitt edge detection and (d) shows the Robert edge detection. ACKNOWLEDGEMENT We sincerely thank Mr. Parameshwar, Mr. Prakash, Mr. Raffi and Mr. Chandrashekar, Coreel, technologies, Bangalore for their support and guidance. REFERENCES [1] Kryjak, T., Komorkiewicz, M., and Gorgon, M., Real-time hardware software embedded vision system for ITS smart camera implemented in Zynq SoC,Journal of Real-Time Image Processing, [2] Mayur Salve, Dinesh Repale, Sanket Shingate and Divya Shah, Simulink based moving object detection and Blob counting algorithm for Traffic Surveillance, International Journal of Computer Applications, Vol. 70, No.17, May [3] R. Revathi and M. Hemalatha, Certain Approach of Object Tracking using Optical Flow Techniques, International Journal of Computer Applications, Vol. 53,No.8, Sep [4] Menakshi Bhat, Pragati Kapoor and B.L.Raina, Application of sad algorithm in image processig for motion detection an d Simulink blocksets for object tracking, International journal of engineering science & advanced technology, Vol. 2, Issue. 3, May-Jun [5] Adhyana gupta. Hardware software co-simulation for traffic load computation using MATLAB Simulink model blockset, International Journal of Computational Science and Information Technology, Vol.1, No.2, May [6] Swapnil G. Kavitkar and Prashant L. Paikrao, FPGA based Image Feature Extraction Using Xilinx System Generator, Internation al Journal of Computer Science and Information Technologies, Vol. 5, No. 3, , [7] Nazia Abdul Majeed and Satheesh Rao, FPGA Based Design Implementation for Detection of Exudates Using XSG, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue. 4, April [8] T. Saidani, D. Dia, W. Elhamzi, M. Atri and R. Tourki, Hardware Co-simulation For Video Processing Using Xilinx System Generator, Proceedings of the World Congress on Engineering,Vol. I, July [9] Swati R. Dixit and Dr.A.Y.Deshmukh, Sobel Edge Detection Implementation using Spartan 3 FPGA and Xilinx System Generator, International Journal of Engineering Research and General Science, Vol. 3, Issue. 4, Jul-Aug [10] K.Naresh and M.Mahender, FPGA based implementation of image edge detection using canny edge detection algorithm, Internation al journal of scientific engineering and technology research, Vol. 03,Issue. 29,Oct [11] M. Dinesh Chandra, Muni Praveena Rela and DR. M.Gurunadha Babu, Edge detection algorithm for video processing system using FPGA, International Journal of Emerging Trends in Electrical and Electronics,Vol. 10, Issue. 8, Sep All Rights Reserved Page 269

Implementation of Canny Edge Detection Algorithm on FPGA and displaying Image through VGA Interface

Implementation of Canny Edge Detection Algorithm on FPGA and displaying Image through VGA Interface Azra Tabassum 1, Harshitha P 2, Sunitha R 3 1-2 8 th sem Student, Dept of ECE, RRCE, Bangalore, Karnataka,