Cataloged from PDF version of article.Video surveillance has long been in use to monitor security sensitive areas such
as banks, department stores, highways, crowded public places and borders. 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.
In this thesis, a smart visual surveillance system with real-time moving object
detection, classification and tracking capabilities is presented. The system
operates on both color and gray scale video imagery from a stationary camera.
It can handle object detection in indoor and outdoor environments and under
changing illumination conditions. The classification algorithm makes use of the
shape of the detected objects and temporal tracking results to successfully categorize
objects into pre-defined classes like human, human group and vehicle.
The system is also able to detect the natural phenomenon fire in various scenes
reliably. The proposed tracking algorithm successfully tracks video objects even
in full occlusion cases. In addition to these, some important needs of a robust smart video surveillance system such as removing shadows, detecting sudden illumination
changes and distinguishing left/removed objects are met.Dedeoğlu, YiğithanM.S