Unusual scene detection using distributed behaviour model and sparse representation

Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2012) Unusual scene detection using distributed behaviour model and sparse representation. In 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS), Institute of Electrical and Electronics Engineers (IEEE), Beijing, China, pp. 48-53.

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The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.

Impact and interest:

5 citations in Scopus
3 citations in Web of Science®
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34 since deposited on 26 Jun 2012
12 in the past twelve months

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ID Code: 51042
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
DOI: 10.1109/AVSS.2012.80
ISBN: 9781467324991
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Past > Institutes > Information Security Institute
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 IEEE
Deposited On: 26 Jun 2012 01:18
Last Modified: 28 Jun 2017 20:19

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