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Proceedings Paper

Real-time multiple-object tracking and anomaly detection
Author(s): Mei Han; Yihong Gong
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Paper Abstract

In this paper we describe a real time video surveillance system which is capable of tracking multiple objects simultaneously and detecting violations. The number of objects is unknown and varies during tracking. Based on preliminary results of object detection in each image which may have missing and/or false detections, the multiple object tracking algorithm keeps a graph structure where it maintains multiple hypotheses about the number and the trajectories of the objects in the video. The image information drives the process of extending and pruning the graph, and determines the best hypothesis to explain the video. The multiple object tracking algorithm gives feedbacks which are predictions of object locations to the object detection module. Therefore, the algorithm integrates object detection and tracking tightly. The most possible hypothesis provides the multiple object tracking result which is used to accomplish anomaly detection. The trajectories generated by the tracking algorithm provide information of object identifications, motion histories, timing at sensitive areas and object interactions. The system has been running at a few access control areas for more than eighteen months. Experimental results on human tracking are presented and applications to anomaly detection are described.

Paper Details

Date Published: 17 January 2005
PDF: 10 pages
Proc. SPIE 5682, Storage and Retrieval Methods and Applications for Multimedia 2005, (17 January 2005); doi: 10.1117/12.587951
Show Author Affiliations
Mei Han, NEC Labs. America (United States)
Yihong Gong, NEC Labs. America (United States)


Published in SPIE Proceedings Vol. 5682:
Storage and Retrieval Methods and Applications for Multimedia 2005
Rainer W. Lienhart; Noboru Babaguchi; Edward Y. Chang, Editor(s)

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