Abstract
Design of automated video surveillance systems is one of the exigent missions in computer vision community because of their ability to automatically select frames of interest in incoming video streams based on motion detection. This research paper focuses on the real-time hardware implementation of a motion detection algorithm for such vision based automated surveillance systems. A dedicated VLSI architecture has been proposed and designed for clustering-based motion detection scheme. The working prototype of a complete standalone automated video surveillance system, including input camera interface, designed motion detection VLSI architecture, and output display interface, with real-time relevant motion detection capabilities, has been implemented on Xilinx ML510 (Virtex-5 FX130T) FPGA platform. The prototyped system robustly detects the relevant motion in real-time in live PAL (720 × 576) resolution video streams directly coming from the camera.
Highlights
The importance of motion detection for designing an automated video surveillance system can be gauged from the availability of a large number of robust and complex algorithms that have been developed to-date, and the even larger number of articles that have been published on this topic so far
The problem of motion detection can be stated as “given a set of images of the same scene taken at several different times, the goal of motion detection is to identify the set of pixels that are significantly different between the last image of the sequence and the previous images” [1]
All design design modules of proposed proposed architecture for clustering clustering based motion detection scheme have been coded in VHDL, simulated using
Summary
The importance of motion detection for designing an automated video surveillance system can be gauged from the availability of a large number of robust and complex algorithms that have been developed to-date, and the even larger number of articles that have been published on this topic so far. In order to address this problem of reducing the computational complexity, Chutani and Chaudhury [53] proposed a block-based clustering scheme with a very low complexity for motion detection On one hand, this scheme is robust enough for handling pseudo-stationary nature of background, and on the other it significantly lowers the computational complexity and is well suited for designing standalone systems for real-time applications. This scheme is robust enough for handling pseudo-stationary nature of background, and on the other it significantly lowers the computational complexity and is well suited for designing standalone systems for real-time applications For this reason we have selected the clustering based motion detection scheme for designing the real-time standalone motion detection. The implemented motion detection system can be used as a standalone system for automated video surveillance applications
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