Abstract
Crowd behavior analysis is a challenging task for computer vision. In this paper, we present a novel approach for crowd behavior analysis and anomaly detection in coherent and incoherent crowded scenes. Two main aspects describe the novelty of the proposed approach: first, modeling the observed flow field in each non-overlapping block through social entropy to measure the concerning uncertainty of underlying field. Each block serves as an independent social system and social entropy determine the optimality criteria. The resulted in distributions of the flow field in respective blocks are accumulated statistically and the flow feature vectors are computed. Second, Support Vector Machines are used to train and classify the flow feature vectors as normal and abnormal. Experiments are conducted on two benchmark datasets PETS 2009 and University of Minnesota to characterize the specific and overall behaviors of crowded scenes. Our experiments show promising results with 95.6% recognition rate for both the normal and abnormal behavior in coherent and incoherent crowded scenes. Additionally, the similar method is tested using flow feature vectors without incorporating social entropy for comparative analysis and the detection results indicate the dominating performance of the proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Helbing, D., Johansson, A., Al-Abideen, H.Z.: The dynamics of crowd disasters: An empirical study. An Empirical Study. Phys. Rev. 75, 046109 (2007)
Ferryman, J.: (PETS 2007), http://www.cvg.rdg.ac.uk/PETS2007
Wu, S., Moore, B.E., Shah, M.: Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (2010)
Ge, W., Collins, R.: Marked point processes for crowd counting. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2913–2920 (2009)
Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 1–14. Springer, Heidelberg (2008)
Zhan, B., Monekosso, D., Remagnino, P., Velastin, S., Xu, L.: Crowd analysis: A survey. Machine Vision Application 19, 345–357 (2008)
Andrade, E.L., Scott, B., Fisher, R.B.: Hidden markov models for optical flow analysis in crowds. In: Proceedings of the 18th International Conference on Pattern Recognition, pp. 460–463. IEEE Computer Society, Los Alamitos (2006)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 935–942 (2009)
Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)
Boghossian, A., Velastin, A.: Motion-based machine vision techniques for the management of large crowds. In: Proceedings of 6th IEEE International Conference on ICECS Electronics, Circuits and Systems, vol. 2, pp. 961–964 (2002)
Maurin, B., Masoud, O., Papanikolopoulos, N.: Monitoring crowded traffic scenes. In: 5th IEEE International Conference on Intelligent Transportation Systems, pp. 19–24 (2002)
Albiol, A., Silla, M., Albiol, A., Mossi, J.: Video analysis using corner motion statistics. In: Performance Evaluation of Tracking and Surveillance Workshop at CVPR 2009, pp. 31–37 (2009)
Benabbas, Y., Ihaddadene, N., Djeraba, C.: Global analysis of motion vectors for event detection in crowd scenes. In: Performance Evaluation of Tracking and Surveillance Workshop at CVPR 2009, pp. 109–116 (2009)
Bruhn, A., Weickert, J., Schnörr, C.: Combining the advantages of local and global optic flow methods. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 454–462. Springer, Heidelberg (2002)
Bailey, K.D.: Social entropy theory: An overview. Journal Systemic Practice and Action Research 3, 365–382 (1990)
Al-Hamadi, A., Michaelis, B.: An intelligent paradigm for multi-objects tracking in crowded environment. Journal of Digital Information Management 4, 183–190 (2006)
Bouguet, J.Y.: Pyramidal implementation of the lucas kanade feature tracker: Description of the algorithm (2002)
Shannon, C.E.: A Mathematical Theory of Communication. University of Illinois Press, US (1949)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines: and Other Kernel-based Learning Methods, 1st edn. Cambridge University Press, Cambridge (2000)
Ferryman, J., Shahrokni, A.: PETS 2009 (2009), http://www.cvg.rdg.ac.uk/PETS2009
UMN: (Detection of unusual crowd activity), http://mha.cs.umn.edu
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pathan, S.S., Al-Hamadi, A., Michaelis, B. (2010). Incorporating Social Entropy for Crowd Behavior Detection Using SVM. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-17289-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17288-5
Online ISBN: 978-3-642-17289-2
eBook Packages: Computer ScienceComputer Science (R0)