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Passenger Flow Detection of Video Surveillance: A Case Study of High-Speed Railway Transport Hub in China
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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
Kaunas University of Technology (KTU)
Title: Passenger Flow Detection of Video Surveillance: A Case Study of High-Speed Railway Transport Hub in China
Description:
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.
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