Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30309
ADABeV: Automatic Detection of Abnormal Behavior in Video-surveillance

Authors: Nour Charara, Iman Jarkass, Maria Sokhn, Elena Mugellini, Omar Abou Khaled

Abstract:

Intelligent Video-Surveillance (IVS) systems are being more and more popular in security applications. The analysis and recognition of abnormal behaviours in a video sequence has gradually drawn the attention in the field of IVS, since it allows filtering out a large number of useless information, which guarantees the high efficiency in the security protection, and save a lot of human and material resources. We present in this paper ADABeV, an intelligent video-surveillance framework for event recognition in crowded scene to detect the abnormal human behaviour. This framework is attended to be able to achieve real-time alarming, reducing the lags in traditional monitoring systems. This architecture proposal addresses four main challenges: behaviour understanding in crowded scenes, hard lighting conditions, multiple input kinds of sensors and contextual-based adaptability to recognize the active context of the scene.

Keywords: Pattern Recognition, Data fusion, Behavior recognition, Crowded scene, Video-surveillance

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057171

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3220

References:


[1] A. M. Cheriyadat and R. Radke, "Detecting dominant motions in dense crowds," IEEE J. Select. Topics Signal Process, vol. 2, no. 4, pp. 568- 581, Aug. 2008.
[2] T. Soumya, "A Moving object segmentation method for low illumination night videos," in Proceeding of WCECS, October 22-24, San Francisco, USA, 2008.
[3] R. Mehran, A. Oyama, and M. Shah, "Abnormal crowd behavior detection using social force model," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[4] G. J. Brostow and R. Cipolla, "Unsupervised Bayesian detection of independent motion in crowds," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, DC, pp. 594-60, 2006.
[5] W. Lin, M.T. Sun, R. Poovendran, Z. Zhang, "Group Event Detection for Video Surveillance," in Proceedings of ISCAS'2009. pp.2830~2833.
[6] E. L. Andrade, S. Blunsden, and R. B. Fisher, "Modelling crowd scenes for event detection," in Proc. Int. Conf. Pattern Recognition, Washington, DC, pp. 175-178, 2006.
[7] R. Raskar, A. Ilie, and J. Yu, "Image fusion for context enhancement and video surrealism," in Proc. of the 3rd international symposium on Non-photorealistic animation and rendering (NPAR), pp. 85-152. Annecy, France, June 2004.
[8] Y. Cai, K. Huang, T. Tan, and Y. Wang, "Context enhancement of nighttime surveillance by image fusion," in Proceedings of ICPR, pp. 980-983, 2006.
[9] A. Nakazawa, H. Kato, and S. Inokuchi, "Human tracking using distributed vision systems," in Proceedings of the 14thICPR, pp. 593- 596.
[10] J. W. Davis and V. Sharma, "Fusion-Based Background-Subtraction using Contour Saliency," Computer Vision and Pattern Recognition, 20- 26 June, 2005.
[11] H. Torresan, B. Turgeon, C. Ibarra-Castanedo, P. Hébert, X. Maldague, "Advanced Surveillance Systems: Combining Video and Thermal Imagery for Pedestrian Detection," in Proc. of SPIE, Thermosense XXVI, volume 5405 of SPIE, pp. 506-515, April 2004.
[12] D. Gatica-Perez, G. Lathoud, I. McCowan, J. Odobez, and D. Moore, "Audio-visual speaker tracking with importance particle filter," in IEEE International Conference on Image Processing (ICIP03), 2003.
[13] R. Poppe, "A survey on vision-based human action recognition," Image and Vision Computing, 28(6):976-990, 2010.
[14] T. B. Moeslund, A. Hilton, and V. Kruger, "A survey of advances in vision-based human motion capture and analysis," Computer Vision and Image Understanding, 104(2):90-126, 2006.
[15] P. Kumar, S. Ranganath, K. Sengupta, "Behavior Interpretation from Traffic Video Streams," in Proceedings of the IEEE International Conference on Intelligent Transportation Systems". October 12-15, 2003, Shanghai, China, volume 2:pp.1214-1219.
[16] V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos. "Anomaly detection in crowded scenes," in IEEE Conference on Computer Vision and Pattern Recognition, 2010.
[17] P. Smets and R. Kennes, "The transferable belief model," Artificial Intelligence, vol. 66, no. 2, pp. 191-234, Dec. 1994.
[18] M. Guironnet, D. Pellerin, M. Rombaut, "Camera motion classification based on transferable belief model," European Signal Processing Conference (EUSIPCO-2006), Florence, Italy, September 2006.
[19] A. Hakeem, M. Shah, "Multiple Agent Event Detection and Representation in Videos," in Proceedings of American Association for Artificial Intelligence AAAI, 2005.
[20] A. Ilie, R. Raskar, J. Yu, "Gradient domain context enhancement for fixed cameras," in Proc. of ACCV. Jeju Island, Korea, January 2004.
[21] V. T. Vu, F. Bremond, M. Thonnat, "Human behavior visualisation and simulation for automatic video understanding," in Proc. of the 10th Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG-2002), Plzen-Bory, Czech Republic, 2002.
[22] J. Cassens, A. Kofod-Petersen, "Using activity theory to model context awareness: a qualitative study," in Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference, Florida, USA, AAAI Press, 2006.
[23] O. Brdicka, P. Reignier, J. L. Crowley, "Modéliser et faire évoluer le contexte dans des environnements intelligents," In Ingénierie des Systèmes d'Information (ISI), Lavoisier, Vol. 11, No. 5, December 2006.
[24] F. Bremond, M. Thonnat, "Issues of representing context illustrated by video-surveillance applications," in International Journal of Human- Computer Studies - Special issue: using context in applications, Volume 48 Issue 3, March 1998.
[25] T. Strat, "Employing contextual information in computer vision," in DARPA93, pages 217-229, 1993.
[26] H.H. Nagel, "From image sequences towards conceptual descriptions," Image and Vision Computing, 6(2):59-74, 1988.
[27] M. Mohnhaupt, B. Neumann, "Understanding object motion: Recognition, learning and spatiotemporal reasoning," research report FBI-HH-B-145/90, University of Hamburg.
[28] W-S. Zhen, S. Gong, T. Xiang, "Quantifying contextual information for object detection," in IEEE 12th International Conference on Computer Vision, pp.932-939, Sept. 29 2009-Oct. 2 2009. doi: 10.1109/ICCV.2009.545934
[29] N. Dalal, B. Triggs. "Histograms of oriented gradients for human detection," in CVPR, 2005.
[30] G. Heitz, D. Koller. "Learning spatial context: Using stuff to find things," in ECCV, 2008.
[31] A. Adam, E. Rivlin, I. Shimshoni and D. Reinitz, "Robust Real-Time Unusual Event Detection Using Multiple Fixed-Location Monitors," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 3, March 2008.
[32] M. D. Breitenstein, H. Grabner, and L. V. Gool, "Hunting Nessie - realtime abnormality detection from webcams," in IEEE International Workshop on Visual Surveillance, 2009.
[33] S. Saxena, F. Brémond, M. Thonnat, R. Ma. "Crowd Behavior Recognition for Video Surveillance," in Advanced Concepts for Intelligent Vision Systems (ACIVS 08), 2008.