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Detection of Entry/Exit Zones for Visual Surveillance System using Graph Theoretic Clustering  

Woo, Ha-Yong (Dept. of Electronic Engineering, Sogang Univ.)
Kim, Gyeong-Hwan (Dept. of Electronic Engineering, Sogang Univ.)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.46, no.6, 2009 , pp. 1-8 More about this Journal
Abstract
Detecting entry and exit zones in a view covered by multiple cameras is an essential step to determine the topology of the camera setup, which is critical for achieving and sustaining the accuracy and efficiency of multi-camera surveillance system. In this paper, a graph theoretic clustering method is proposed to detect zones using data points which correspond to entry and exit events of objects in the camera view. The minimum spanning tree (MST) is constructed by associating the data points. Then a set of well-formed clusters is sought by removing inconsistent edges of the MST, based on the concepts of the cluster balance and the cluster density defined in the paper. Experimental results suggest that the proposed method is effective, even for sparsely elongated clusters which could be problematic for expectation-maximization (EM). In addition, comparing to the EM-based approaches, the number of data required to obtain stable outcome is relatively small, hence shorter learning period.
Keywords
visual surveillance; clustering; camera topology; minimum spanning tree; entry/exit zones detection;
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1 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification, 2nd ed. NewYork: Wiley, 2001
2 G. Oleksandr, Z. Yan, and J. Zach, 'Minimum Spanning Tree Based Clustering Algorithms,' in Proceedings of IEEE International Conference on Tools with Artificial Intelligence, pp. 73-81, 2006
3 C. T. Zahn, 'Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters,' IEEE Transactions on Computers, vol. 20, pp.68-86, 1971   DOI   ScienceOn
4 P. Foggia, G. Percannella, C. Sansone, and M. Vento, 'Benchmarking graph-based clustering algorithms,' Image and Vision Computing, vol. 27, pp.979-988, 2009   DOI   ScienceOn
5 D. Makris, T. Ellis, and J. Black, 'Bridging the gaps between cameras,' in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.2, pp.II205-II210, 2004
6 W. Kang and F. Deng, 'Research on Intelligent Visual Surveillance for Public Security,' in Proceedings of the IEEE/ACIS International Conference on Computer and Information Science, pp. 824-829, 2007
7 D. Makris and T. Ellis, 'Automatic learning of an activity-based semantic scene model,' in Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance, pp.183-188, 2003
8 D. J. Marchette, Random graphs for statistical pattern recognition. Hoboken, N.J.: Wiley- Interscience, 2004
9 C. Stauffer, 'Estimating Tracking Sources and Sinks,' in Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, pp.35-35, 2003