An FPGA based hardware accelerator for real time video segmentation system

Video Segmentation is the basic requirement for applications such as video surveillance, traffic management and medical imaging. The high computation power must be provided to support this operation. This paper discuss on design and implementation of hardware accelerator for video segmentation. The algorithm of Sobel edge detection operator is used to develop this hardware accelerator. To develop hardware accelerator datapath architecture the management of memory access is deployed and architecture based pipeline are made with the potential improvements in acceleration to the read data pixel from memory. In addition, a finite state machine is used to ensure the hardware accelerator controls the sequence of derivative computation, the write and read operations. System Integration uses NIOS II processor and Avalon bus interfacing. The hardware accelerator design was implemented on an Altera FPGA development board and has managed to achieve a video rate of 30 frames per second as required by NTSC standard definition video. The architecture core for memory access has succeeded to reduce memory bandwidth utility by 75%.

[1]  M. Boo,et al.  VLSI implementation of an edge detector based on Sobel operator , 1994, Proceedings of Twentieth Euromicro Conference. System Architecture and Integration.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Madhukar Budagavi Real-time image and video processing in portable and mobile devices , 2006, Journal of Real-Time Image Processing.

[4]  A. Rosenfeld,et al.  Computer vision: a source of models for biological visual processes? , 1989, IEEE Transactions on Biomedical Engineering.

[5]  Stephan Hussmann,et al.  A high-speed subpixel edge detector implementation inside a FPGA , 2003, Real Time Imaging.

[6]  J. S. Sohal,et al.  Performance Evaluation of Prewitt Edge Detector for Noisy Images , 2006 .

[7]  N.P. Rath,et al.  Performance Analysis of Fuzzy-Based Canny Edge Detector , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[8]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.