Detecting Humans in Video Footage using Multiple Classifiers
James Stanley Russell · 2004
Manual analysis of video content such as surveillance footage is a tedious task as it requires high levels of concentration over long periods of time. Systems capable of automatically detecting and tracking humans can assist or replace human operators by providing a more accurate and cost effective solution. Such systems are currently in high demand due to the emphasis on security and antiterrorism strategies, and the increasing availability and affordability of security cameras. Many other applications such as the archiving of surveillance footage, vision-based user interfaces, and people counting also rely on the ability to detect and track humans. In this thesis, I propose a system that detects foreground objects using a background subtraction technique; tracks the objects using a mean shift based algorithm; then detects humans using a combination of classifiers utilising visual features such as skin colour, body shape, and the periodic nature of the walking motion. By combining a number of independent classifiers a more robust system capable of handling occlusions and changes in human orientation is produced.