Human Behavior Modeling in Video Surveillance of Conference Halls

Authors: Nour Charara, Hussein Charara, Omar Abou Khaled, Hani Abdallah, Elena Mugellini

Abstract:

In this paper, we present a human behavior modeling approach in videos scenes. This approach is used to model the normal behaviors in the conference halls. We exploited the Probabilistic Latent Semantic Analysis technique (PLSA), using the 'Bag-of-Terms' paradigm, as a tool for exploring video data to learn the model by grouping similar activities. Our term vocabulary consists of 3D spatio-temporal patch groups assigned by the direction of motion. Our video representation ensures the spatial information, the object trajectory, and the motion. The main importance of this approach is that it can be adapted to detect abnormal behaviors in order to ensure and enhance human security.

Keywords: Activity modeling, clustering, PLSA, video representation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132122

References:

[1] A. Adam, E. Rivlin, I. Shimshoni et D. Reinitz. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3):555–560, 2008.
[2] A. Torralba, K. P. Murphy et W. Freeman. Contextual models for object detection using boosted random fields. In Advances in neural information processing systems (NIPS). MIT Press, Cambridge, MA, pp 1401–1408, 2004.
[3] V. Reddy, C. Sanderson, A. Sanin, B et C. Lovell. MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos. CoRR abs/1406.5095, 2014.
[4] E. L. Andrade, S. Blunsden et R. B. Fisher. Modelling crowd scenes for event detection. Int. Conf. Pattern Recognition, Washington, DC, pp. 175–178, 2006.
[5] P.-M. Jodoin, J. Konrad et V. Saligrama. Modeling background activity for behavior subtraction. In International Conference on Distributed Smart Cameras, pages 1–10, 2008.
[6] C. Li, Z. Han, Q. Ye et J. Jiao. Abnormal behavior detection via sparse reconstruction analysis of trajectory. In the 6th International Conference on Image and Graphics (ICIG ’11), pp. 807–810, 2011.
[7] C. Rougier, J. Meunier, A. St-Arnaud et J. Rousseau. Fall detection from human shape and motion history using video surveillance. In Proceedings Advanced Information Networking and Applications Workshops, 2007.
[8] S. Ali et M. Shah. A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1–6, 2007
[9] A. Nasution et S. Emmanuel. Intelligent video surveillance for monitoring elderly in home environments. In IEEE Workshop on Multimedia Signal Processing, 2007.
[10] M. Aubry, D. Maturana, A. Efros, B. Russell et J. Sivic. Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models. In CVPR 2014.
[11] C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau. Monocular 3d head tracking to detect falls of elderly people. In Proceedings IEEE Conference of the Engineering in Medicine and Biology Society, 2006.
[12] L. Lin, Y. Seo, M. Gen et R. Cheng. Unusual human behavior recognition using evolutionary technique. Computers and Industrial Engineering 56, 1137-1153, 2009.
[13] N. Neverova, C. Wolf, G. W. Taylor, F. Nebout. ModDrop: adaptive multi-modal gesture recognition. Minor revision at IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014.
[14] M. Sivarathinabala et S. Abirami. An Intelligent Video Surveillance Framework for Remote Monitoring. International Journal of Engineering Science and Innovative Technology (IJESIT). Volume 2, Issue 2, 2013..
[15] J. Varadarajan et J.M. Odobez. Topic models for scene analysis and abnormality detection. In Proceedings of the International Conference on Computer Vision - Workshop on Visual Surveillance, Kyoto, 2009.
[16] Y. Wang, D. Wang et F. Chen. Abnormal Behavior Detection Using Trajectory Analysis in Camera Sensor Networks, International Journal of Distributed Sensor Networks, Article ID 839045, 9 pages, 2014.
[17] R. Ge, Z. Shan, H. Kou. An Intelligent Surveillance system Based on Motion detection, Proceedings of IEEE, IC-BNMT 2011.
[18] J. Varadarajan, R. Emonet, et J. Odobez. Bridging the Past, Present and Future; Modeling Scene Activities from Event Relationships and Global Rules. In IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012.
[19] R. Mehran, A. Oyama et M. Shah. Abnormal crowd behaviour detection using social force model. In IEEE Conference on Computer Vision and Pattern Recognition, pages 935–942, 2009.
[20] J. Li, S. Gong, et T. Xiang. Discovering multi-camera behaviour correlations for on-the fly global activity prediction and anomaly detection. In IEEE International Workshop on Visual Surveillance, Kyoto, Japan, 2009.
[21] J. F. P. Kooij, G. Englebienne, D. M. Gavrila. A Non-parametric Hierarchical Model to Discover Behavior Dynamics from Tracks, Computer Vision – ECCV 2012 Lecture Notes in Computer Science, volume 7577, pp 270-283, 2012.
[22] B. Klare et S. Sarkar. Background subtraction in varying illuminations using an ensemble based on an enlarged feature set. Computer Vision and Pattern Recognition Workshop, 0:66–73, 2009.
[23] B.D. Lucas et T. Kanade. An iterative image registration technique with an application to stereo vision. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 674–679, 1981.