Research On Moving Object Classification In Video Surveillance

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. Objects classification is an important aspect of intelligent video surveillance, and its research content is to classify moving objects into semantically meaningful categories, associating the correct object class label with the region of interest.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 static features of the detected objects and temporal context to successfully categorize objects into 6 pre-defined classes. Experimental results demonstrate that the proposed classifying algorithms satisfactory performance in a variety of surveillance videos.