Abstract
In this paper, we propose an unsupervised approach for learning the three factors of the topology of a non-overlapping multi-camera network, which are nodes, links, and transition time distributions. It is a cross-correlation based method. Different from previous methods, the proposed method can deal with large amounts of data without considering the size of time window. The connectivity between nodes is estimated based on the N-neighbor accumulated cross-correlations, as well as the transition time distribution for each link. Furthermore, integrated with similarity cues, the proposed method can be extended into weighted cross-correlation models for better performance. Experimental results both on simulated and real-life datasets demonstrate the effectiveness of the proposed method.
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References
Ellis, T.J., Makris, D., Black, J.K.: Learning a Multi-camera Topology. In: Joint IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pp. 165–171 (2003)
Cai, Y., Huang, K., Tan, T., Pietikäinen, M.: Recovering the Topology of Multiple Cameras by Finding Continuous Paths in a Trellis. In: ICPR, pp. 3541–3544 (2010)
Niu, C., Grimson, E.: Recovering Non-overlapping Network Topology using Far-field Vehicle Tracking Data. In: ICPR, pp. 944–949 (2006)
Marinakis, D., Giguère, P., Dudek, G.: Learning network topology from simple sensor data. In: Kobti, Z., Wu, D. (eds.) Canadian AI 2007. LNCS (LNAI), vol. 4509, pp. 417–428. Springer, Heidelberg (2007)
Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across Multiple Cameras with Disjoint Views. In: ICCV, pp. 952–957 (2003)
Tieu, K., Dalley, G., Grimson, W.E.L.: Inference of Non-overlapping Camera Network Topology by Measuring Statistical Dependence. In: ICCV, pp. 1842–1849 (2005)
Nam, Y., Ryu, J., Choi, Y., Cho, W.: Learning Spatio-temporal Topology of a Multi-camera Network by Tracking Multiple People. Proceedings of World Academy of Science, Engineering and Technology 24, 175–180 (2007)
Makris, D., Ellis, T., Black, J.: Bridging the Gaps between Cameras. In: CVPR, vol. 2, pp. 205–210 (2004)
Zou, X., Bhanu, B., Roy-Chowdhury, A.: Continuous Learning of a Multilayered Network Topology in a Video Camera Network. EURASIP Journal on Image and Video Processing (2009)
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Chen, X., Huang, K., Tan, T. (2012). Learning the Three Factors of a Non-overlapping Multi-camera Network Topology. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_14
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DOI: https://doi.org/10.1007/978-3-642-33506-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
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