Abstract
Video Surveillance allows users to easily monitor the secure areas with surveillance cameras, and thus eliminating the need for manual work and saves the huge monitoring costs. A novel method for object tracking, based on image segmentation is proposed to automatically recognize activities around restricted area to improve safety and security of the servicing area by multiplexing hundreds of video streams in real time. Key component for the proposed system includes background learning and updating, foreground segmentation, features extraction, and decision-making process. The proposed method uses adaptive background subtraction techniques to handle illumination changes to improve the performance of the video surveillance and video- enable operations. The algorithms are simulated using MATLAB tool to verify its stability in various conditions by giving various input video samples and its output is taken as benchmark for real time implementation in DSP processor.
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