ADABeV: Automatic Detection of Abnormal Behavior in Video-surveillance
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:51547", author = "Nour Charara and Iman Jarkass and Maria Sokhn and Elena Mugellini and Omar Abou Khaled", title = "ADABeV: Automatic Detection of Abnormal Behavior in Video-surveillance", 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 = "Behavior recognition, Crowded scene, Data fusion,
Pattern recognition, Video-surveillance", volume = "6", number = "8", pages = "938-7", }
