A study on implementing autonomous video surveillance systems based on optical flow concept

Autonomous video surveillance systems are usually built with several functional blocks such as motion detection, foreground and background separation, object tracking, depth estimation, feature extraction and behavioral analysis of tracked objects. Each of those blocks is usually designed with different techniques and algorithms, which may need significant computational and hardware resources. In this thesis we present a surveillance system based on an optical flow concept, as a main unit on which other functional blocks depend. Optical flow limitations, capabilities and possible problem solutions are discussed in this thesis. Moreover, performance evaluation of various methods in handling occlusions, rigid and non-rigid object classification, segmentation and tracking is provided for a variety of video sequences under different ambient conditions. Finally, processing time is measured with software that shows an optical flow hardware block can improve system performance and increase scalability while reducing the processing time by more than fifty percent.