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

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

[1]  John Yen.  Evidential reasoning in expert system , 1986 .

[2]  Shaogang Gong,et al.  Incremental and adaptive abnormal behaviour detection , 2008, Comput. Vis. Image Underst..

[3]  Takeo Kanade,et al.  Object Detection Using the Statistics of Parts , 2004, International Journal of Computer Vision.

[4]  N.H.C. Yung,et al.  Vehicle shape approximation from motion for visual traffic surveillance , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[5]  Ying Chen,et al.  Comparative Study on Moving Object Detection Method of Video Surveillance System in China High-Speed Railway Transport Hub , 2011 .

[6]  Yoshiaki Shirai,et al.  Real-Time Surveillance System Detecting Persons in Complex Scenes , 2001, Real Time Imaging.

[7]  Jia Limin.  Research on Railway Invasion Detection Technology Based on Intelligent Video Analysis , 2010 .

[8]  Tieniu Tan,et al.  Recent developments in human motion analysis , 2003, Pattern Recognit..

[9]  Stavros J. Perantonis,et al.  Detecting abnormal human behaviour using multiple cameras , 2009, Signal Process..

[10]  Duan-Yu Chen,et al.  Motion-based unusual event detection in human crowds , 2011, J. Vis. Commun. Image Represent..

[11]  Mahnaz Janipoor Deilamani,et al.  Moving object tracking based on mean shift algorithm and features fusion , 2011, 2011 International Symposium on Artificial Intelligence and Signal Processing (AISP).

[12]  Yuan Jian,et al.  Prospects and Current Studies on Background Subtraction Techniques for Moving Objects Detection from Surveillance Video , 2006 .

[13]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[14]  PeopleIsmail,et al.  W 4 : Who ? When ? Where ? What ? A Real Time System for Detecting and Tracking , 1998 .

[15]  Joachim Denzler,et al.  Model based extraction of articulated objects in image sequences for gait analysis , 1997, Proceedings of International Conference on Image Processing.

[16]  Luciano da Fontoura Costa,et al.  Estimating crowd density with Minkowski fractal dimension , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[17]  S. Murali,et al.  Segmentation of motion objects from surveillance video sequences using partial correlation , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).