Background Modeling of Moving Object Detection in High-speed Railway Transport Hub Video Surveillance
Xie Zhengyu -, Limin Jia, Qin Yong -, Wang Li · Journal of Convergence Information Technology · 2013
Detect moving object from a video sequence is a fundamental and critical task in many computer vision application. In video surveillance of high-speed railway transport hub, detection of moving object aims to accurately and timely find congestion of passenger flow and other dangerous behaviors in hub. With comparative study on existing methods of moving object detection, a modified background model was proposed in this paper. The model was integrated existing background model with gray division and Dempster-Shafer theory to improve processing speed and accuracy of background modeling. Availability and efficiency of modified model was proved by experiment on data from highspeed railway transport hub video surveillance.