Abstract
This paper presents a novel framework for detecting abnormal pedestrian and vehicle behaviour by modelling cross-correlation among different co-occurring objects both locally and globally in a given scene. We address this problem by first segmenting a scene into semantic regions according to how object events occur globally in the scene, and second modelling concurrent correlations among regional object events both locally (within the same region) and globally (across different regions). Instead of tracking objects, the model represents behaviour based on classification of atomic video events, designed to be more suitable for analysing crowded scenes. The proposed system works in an unsupervised manner throughout using automatic model order selection to estimate its parameters given video data of a scene for a brief training period. We demonstrate the effectiveness of this system with experiments on public road traffic data.
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Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., Maybank, S.: A system for learning statistical motion patterns. PAMI 28 (9), 1450–1464 (2006)
Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. BMVC 2, 583–592 (1995)
Xiang, T., Gong, S.: Beyond tracking: Modelling activity and understanding behaviour. IJCV 67 (1), 21–51 (2006)
Xiang, T., Gong, S.: Video behavior profiling for anomaly detection. PAMI 30(5), 893–908 (2008)
Brand, M., Kettnaker, V.: Discovery and segmentation of activities in video. PAMI 22(8), 844–851 (2000)
Wang, X., Ma, X., Grimson, W.E.L.: Unsupervised activity perception by hierarchical bayesian models. In: CVPR, Minneapolis, USA, June 18-23, pp. 1–8 (2007)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: CVPR, vol. 2, pp. 246–252 (1999)
Russell, D., Gong, S.: Minimum cuts of a time-varying background. In: BMVC, Edinburgh, UK, 1–10 (September 2006)
Gong, S., Xiang, T.: Scene event recognition without tracking. Special issue on visual surveillance, Acta Automatica Sinica 29(3), 321–331 (2003)
Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6(2), 461–464 (1978)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, series B 39(1), 1–38 (1977)
Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: NIPS (2004)
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© 2008 Springer-Verlag Berlin Heidelberg
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Li, J., Gong, S., Xiang, T. (2008). Scene Segmentation for Behaviour Correlation. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88693-8_28
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DOI: https://doi.org/10.1007/978-3-540-88693-8_28
Publisher Name: Springer, Berlin, Heidelberg
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