Passenger Flow Detection of Video Surveillance: A Case Study of High-Speed Railway Transport Hub in China

被引：4
|
作者：
Xie Zhengyu [1 ,2 ]
Jia Limin [2 ]
Qin Yong [2 ]
Wang Li [1 ]
机构：
[1] Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China
[2] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China
来源：
ELEKTRONIKA IR ELEKTROTECHNIKA | 2015年 / 21卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Image analysis; image recognition; background model; passenger flow status; high-speed railway transport hub; SYSTEM;
D O I：
10.5755/j01.eee.21.1.9805
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：48 / 53
页数：6
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