Algorithms for Smart Video Surveillance

The advance in computing power, availability of large- capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of "smart" video surveillance systems has become a critical requirement. The making of video surveillance systems "smart" requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis.

Autorentext

Yigithan Dedeoglu is a Turkish software development engineer and researcher. His research interests include computer vision and computer graphics. As a software developer & researcher he worked for companies including Microsoft, Synectics, Honeywell, Novar. He has published several papers and contributed to computer vision books.