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Proceedings Paper

Unusual behavior detection in the entry gate scenes of subway station using Bayesian networks and inference
Author(s): Sooyeong Kwak; Guntae Bae; Manbae Kim; Hyeran Byun
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Paper Abstract

In this paper, we propose a method for detecting unusual human behavior using monocular camera which is not moving. Our system composed of three modules which are moving object detection, tracking, and event recognition. The key part is event recognition module. We define unusual events which are composed of two simple events (drop off luggage, unattended luggage) and two complex events (abandoned luggage and steal luggage). In order to detect the simple event, we construct Bayesian network in each unusual event. We extract evidences using bounding box properties which are the location of moving objects, speed, distance between the person and the other moving object (such as bag), existing time. And then, we use finite state automaton which shows the temporal relation of two simple events to detect complex events. To evaluate the performance, we compare the frame number when an even is triggered with our results and the ground truth. The proposed algorithm showed good results on the real world environment and also worked at real time speed.

Paper Details

Date Published: 26 February 2008
PDF: 8 pages
Proc. SPIE 6813, Image Processing: Machine Vision Applications, 681311 (26 February 2008); doi: 10.1117/12.766946
Show Author Affiliations
Sooyeong Kwak, Yonsei Univ. (South Korea)
Guntae Bae, Yonsei Univ. (South Korea)
Manbae Kim, Kangwon National Univ. (South Korea)
Hyeran Byun, Yonsei Univ. (South Korea)

Published in SPIE Proceedings Vol. 6813:
Image Processing: Machine Vision Applications
Kurt S. Niel; David Fofi, Editor(s)

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