Incremental behavior modeling and suspicious activity detection

Pattern Recognition

Volumn 46, Issue 3, 2013, Pages 671-680

  Ouivirach, Kan ^a   Gharti, Shashi ^a   Dailey, Matthew N ^a  

^a ASIAN INSTITUTE OF TECHNOLOGY   (Thailand)

Author keywords

Anomaly detection; Behavior clustering; Bootstrapping; Hidden Markov models; Incremental learning; Sufficient statistics

Indexed keywords

ANOMALY DETECTION; BEHAVIOR CLUSTERING; BOOTSTRAPPING; INCREMENTAL LEARNING; SUFFICIENT STATISTICS;

AUTOMATION; ERRORS; HIDDEN MARKOV MODELS; SECURITY SYSTEMS; STATISTICAL TESTS;

DIGITAL STORAGE;

EID: 84870239401     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.10.008     Document Type: Article

References (42)

1. L.S. Davis, S. Fejes, D. Harwood, Y. Yacoob, I. Haratoglu, M.J. Black, Visual surveillance of human activity, in: Asian Conference on Computer Vision (ACCV), 1998, pp. 267-274.
2. O. Masoud, N. Papanikolopoulos, Recognizing human activities, in: International on Advanced Video and Signal Based Surveillance (AVSS), 2003, p. 157.
3. D. Cao, O.T. Masoud, D. Boley, N. Papanikkolopoulos, Online motion classification using support vector machines, in: International Conference on Robotics and Automation (ICRA), 2004, pp. 2291-2296.
4. C. Sminchisescu, A. Kanaujia, Z. Li, D. Metaxas, Conditional random fields for contextual human motion recognition, in: International Conference on Computer Vision (ICCV), 2005, pp. 1808-1815.
5. H. Li, Z. Hu, Y. Wu, F. Wu, Behavior modeling and recognition based on space-time image features, in: International Conference on Pattern Recognition (ICPR), 2006, pp. 243-246.
6. Y. Du, F. Chen, W. Xu, Y. Li, Recognizing interaction activities using dynamic bayesian network, in: International Conference on Pattern Recognition (ICPR), 2006, pp. 618-621.
7. S.B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian, Hidden conditional random fields for gesture recognition, in: International Conference on Computer Vision and Pattern Recognition (CVPR), 2006, pp. 1521-1527.
8. J. Yamato, J. Ohya, K. Ishii, Recognizing human action in time-sequential images using hidden Markov model, in: International Conference on Computer Vision and Pattern Recognition (CVPR), 1992, pp. 379-385.
9. J. Gao, A.G. Hauptmann, A. Bharucha, H.D. Wactlar, Dining activity analysis using a hidden Markov model, in: International Conference on Pattern Recognition (ICPR), 2004, pp. 915-918.
10. T. Xiang, S. Gong, Video behaviour profiling and abnormality detection without manual labelling, in: International Conference on Computer Vision (ICCV), 2005, pp. 1238-1245.
11. D.L. Vail, C. Guestrin, Conditional random fields for activity recognition, in: International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2007, pp. 1-8.
12. V. Nair, J. Clark, Automated visual surveillance using hidden Markov models, in: Vision Interface Conference, 2002, pp. 88-92.
13. K.K. Lee, M. Yu, Y. Xu, Modeling of human walking trajectories for surveillance, in: International Conference on Intelligent Robots and Systems (IROS), 2003, pp. 1554-1559.
14. X. Wu, Y. Ou, H. Qian, Y. Xu, A detection system for human abnormal behavior, in: International Conference on Intelligent Robots and Systems (IROS), 2005, pp. 1204-1208.
15. D. Arsić, B. Schuller, G. Rigoll, Suspicious behavior detection in public transport by fusion of low-level video descriptors, in: International Conference on Multimedia and Expo (ICME), 2007, pp. 2018-2021.
16. H. Zhong, M. Visontai, J. Shi, Detecting unusual activity in video, in: International Conference on Computer Vision and Pattern Recognition (CVPR), 2004, pp. 819-826.
17. H. Sakoe Dynamic programming algorithm optimization for spoken word recognition IEEE Transactions on Acoustics, Speech, and Signal Processing 1978 43 49
18. V. Hautamäki, P. Nykänen, P. Fränti, Time-series clustering by approximate prototypes, in: International Conference on Pattern Recognition (ICPR), 2008, pp. 1-4.
19. T. Xiang, and S. Gong Spectral clustering with eigenvector selection Pattern Recognition 41 3 2008 1012 1029
20. E. Swears, A. Hoogs, A. Perera, Learning motion patterns in surveillance video using HMM clustering, in: IEEE Workshop on Motion and Video Computing (WMVC), 2008, pp. 1-8.
21. T. Xiang, and S. Gong Activity based surveillance video content modelling Pattern Recognition 41 7 2008 2309 2326
22. R. Turner, Z. Ghahramani, S. Bottone, Fast online anomaly detection using scan statistics, in: Machine Learning for Signal Processing (MLSP), 2010, pp. 385-390.
23. Y. Yao, A. Sharma, L. Golubchik, and R. Govindan Online anomaly detection for sensor systems a simple and efficient approach Performance Evaluation 67 11 2010 1059 1075
24. A. Rasoulifard, A. Ghaemi Bafghi, M. Kahani, Incremental hybrid intrusion detection using ensemble of weak classifiers, in: International CSI Computer Conference (CSICC), 2008.
25. K. Burbeck, S. Nadjm-Tehrani, Adwice - anomaly detection with real-time incremental clustering, in: International Conference on Information Security and Cryptology (ICISC), Springer-Verlag, 2004.
26. K. Burbeck, and S. Nadjm-Tehrani Adaptive real-time anomaly detection with incremental clustering Information Security Technical Report 12 2007 56 67
27. F. Ren, L. Hu, H. Liang, X. Liu, W. Ren, Using density-based incremental clustering for anomaly detection, in: International Conference on Computer Science and Software Engineering (CSSE), vol. 3, 2008, pp. 986-989.
28. C. Zhong, N. Li, Incremental clustering algorithm for intrusion detection using clonal selection, in: Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA), 2008, pp. 326-331.
29. J. Ekman, A. Holst, Incremental Stream Clustering and Anomaly Detection, Technical Report, SICS Technical Report T2008:01, 2008.
30. D.A. Vasquez Govea, T. Fraichard, and C. Laugier Growing hidden Markov models an incremental tool for learning and predicting human and vehicle motion International Journal of Robotics Research (IJRC) 28 11-12 2009 1486 1506
31. D. Kulić, Y. Nakamura, Incremental learning of human behaviors using hierarchical hidden Markov models, in: International Conference on Intelligent Robots and Systems (IROS), 2010, pp. 4649-4655.
32. T. Xiang, and S. Gong Incremental and adaptive abnormal behaviour detection Computer Vision and Image Understanding (CVIU) 111 1 2008 59 73
33. K. Ouivirach, M.N. Dailey, Clustering human behaviors with dynamic time warping and hidden Markov models for a video surveillance system, in: ECTI-CON, 2010, pp. 884-888.
34. C. Poppe, G. Martens, P. Lambert, R.V. de Walle, Improved background mixture models for video surveillance applications, in: Asian Conference on Computer Vision (ACCV), 2007, pp. 251-260.
35. G. Pass, R. Zabih, J. Miller, Comparing images using color coherence vectors, in: International Conference on Multimedia, 1996, pp. 65-73.
36. T. Oates, L. Firoiu, P.R. Cohen, Using dynamic time warping to bootstrap HMM-based clustering of time series, in: Sequence Learning: Paradigms, Algorithms, and Applications, 2001, pp. 35-52.
37. L.R. Rabiner A tutorial on hidden Markov models and selected applications in speech recognition IEEE 1989 257 286
38. Y. Gotoh, H.F. Silverman, Incremental ML estimation of HMM parameters for efficient training, in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1996, pp. 585-588.
39. R.M. Neal, G.E. Hinton, A new view of the EM algorithm that justifies incremental and other variants, in: Learning in Graphical Models, 1993, pp. 355-368.
40. S.J. Nowlan, Soft Competitive Adaptation: Neural Network Learning Algorithms Based on Fitting Statistical Mixtures, Ph.D. Thesis, Carnegie Mellon University, 1991.
41. K. Ouivirach, S. Gharti, M.N. Dailey, Automatic suspicious behavior detection from a small bootstrap set, in: VISAPP, 2012, pp. 655-658.
42. P. Coombes, ZoneMinder: a Linux-based camera monitoring and analysis tool, http://www.zoneminder.com, 2007.