Memory Efficient VLSI Implementation of Real-Time Motion Detection System Using FPGA Platform
AbstractMotion detection is the heart of a potentially complex automated video surveillance system, intended to be used as a standalone system. Therefore, in addition to being accurate and robust, a successful motion detection technique must also be economical in the use of computational resources on selected FPGA development platform. This is because many other complex algorithms of an automated video surveillance system also run on the same platform. Keeping this key requirement as main focus, a memory efficient VLSI architecture for real-time motion detection and its implementation on FPGA platform is presented in this paper. This is accomplished by proposing a new memory efficient motion detection scheme and designing its VLSI architecture. The complete real-time motion detection system using the proposed memory efficient architecture along with proper input/output interfaces is implemented on Xilinx ML510 (Virtex-5 FX130T) FPGA development platform and is capable of operating at 154.55 MHz clock frequency. Memory requirement of the proposed architecture is reduced by 41% compared to the standard clustering based motion detection architecture. The new memory efficient system robustly and automatically detects motion in real-world scenarios (both for the static backgrounds and the pseudo-stationary backgrounds) in real-time for standard PAL (720 × 576) size color video. View Full-Text
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Singh, S.; Mandal, A.S.; Shekhar, C.; Vohra, A. Memory Efficient VLSI Implementation of Real-Time Motion Detection System Using FPGA Platform. J. Imaging 2017, 3, 20.
Singh S, Mandal AS, Shekhar C, Vohra A. Memory Efficient VLSI Implementation of Real-Time Motion Detection System Using FPGA Platform. Journal of Imaging. 2017; 3(2):20.Chicago/Turabian Style
Singh, Sanjay; Mandal, Atanendu S.; Shekhar, Chandra; Vohra, Anil. 2017. "Memory Efficient VLSI Implementation of Real-Time Motion Detection System Using FPGA Platform." J. Imaging 3, no. 2: 20.
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