Fonseca, Alvaro A.

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.