Abstract
Detect moving object from a video sequence is a fundamental and critical task in many computer vision applications. With video surveillance system of high-speed railway transport hub, one of the aims for passenger flow detection is to accurately and promptly detect potential safety hazard hidden in passenger flow. In this paper, a procedure of passenger flow detection in high-speed railway transport hub is presented. According to the key steps of procedure, a modified background model based on Dempster-Shafer theory, and a passenger flow status recognition algorithm based on features of image connected domain are proposed to improve the accuracy and real-time performance of passenger flow detection. Credit and effects of proposed methods were proved by experiment on data from high-speed railway transport hub video surveillance system. DOI: http://dx.doi.org/10.5755/j01.eee.21.1.9805
Highlights
Any vision-based system involving tracking, interpretation and recognition of moving objects in a motion picture requires fast and reliable moving object detection method
We choose passenger flow of high-speed railway transport hub in China as the detecting object, and propose a background model of passenger flow detection based on Dempster-Shafer, and a passenger flow status recognition algorithm based on feature of image connected domain by the characteristics analysis of passenger flow detection area, and a procedure design of passenger flow detection
The characteristics mentioned above are the vital references for high-speed railway transport hub video surveillance to choose a suitable moving object detection method
Summary
Any vision-based system involving tracking, interpretation and recognition of moving objects in a motion picture requires fast and reliable moving object detection method. A comprehensive survey of research on computer-vision-based human motion analysis was provided in [1] It groups moving object detection methods into four categories: temporal differencing [11], optical flow [12], statistical methods [13] and background subtraction [14], [15]. We choose passenger flow of high-speed railway transport hub in China as the detecting object, and propose a background model of passenger flow detection based on Dempster-Shafer, and a passenger flow status recognition algorithm based on feature of image connected domain by the characteristics analysis of passenger flow detection area, and a procedure design of passenger flow detection. The characteristics mentioned above are the vital references for high-speed railway transport hub video surveillance to choose a suitable moving object detection method
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