Abstract
Motion 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.
Highlights
Motion detection, one of the fundamental and most important problem of computer vision, plays a very important role in the realization of a complete vision based automated video surveillance system for automatic scene analysis, monitoring, and generation of security alerts based on relevant motion in a video scene
After analyzing the synthesis results, it is found that proposed architecture for clustering based motion detection scheme utilizes 168 36Kb Block RAMs out of the total 298 Block RAMs are on a Xilinx ML510 (Virtex-5 FX130) FPGA board
VLSI/hardware architecture were coded in VHDL and simulated using ModelSim
Summary
One of the fundamental and most important problem of computer vision, plays a very important role in the realization of a complete vision based automated video surveillance system for automatic scene analysis, monitoring, and generation of security alerts based on relevant motion in a video scene. After analyzing the synthesis results, it is found that proposed architecture for clustering based motion detection scheme utilizes 168 36Kb Block RAMs out of the total 298 (approximately 56%) Block RAMs are on a Xilinx ML510 (Virtex-5 FX130) FPGA board This implies that a large amount of on-chip memory (Block RAMs) is utilized by motion detection system, which is only one of the potentially complex and important components of an automated video surveillance system. FPGA platform—as not much FPGA Block RAMs are left for other complex operations such as focused region extraction, object tracking, and video history generation For this reason, further emphasis needs to be given to the minimization of memory requirements of clustering-based motion detection algorithm and architecture without compromising on accuracy and robustness of motion detection. We have integrated the implemented architectural modules with the camera interface module and DVI display controller and a working prototype system has been developed for real-time motion detection in a video scene
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