Unsupervised video anomaly detection using feature clustering
Unsupervised video anomaly detection using feature clustering
- Author(s): H. Li ; A. Achim ; D. Bull
- DOI: 10.1049/iet-spr.2011.0074
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- Author(s): H. Li 1 ; A. Achim 1 ; D. Bull 1
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View affiliations
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Affiliations:
1: Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK
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Affiliations:
1: Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK
- Source:
Volume 6, Issue 5,
July 2012,
p.
521 – 533
DOI: 10.1049/iet-spr.2011.0074 , Print ISSN 1751-9675, Online ISSN 1751-9683
This study addresses the problem of automatic anomaly detection for surveillance applications. A general framework for anomalous event detection in uncrowded scenes has been developed which consists of the following key components: (i) an efficient foreground detection model based on a Gaussian mixture model (GMM), which can selectively update pixel information in each image region; (ii) an adaptive foreground object tracker that combines the merits of Kalman, mean-shift and particle filtering; (iii) a feature clustering algorithm, which can automatically choose the optimal number of clusters in the training data for scene pattern modelling; (iv) a statistical scene modeller based on Bayesian theory and GMM, which combines trajectory-based and region-based information for enhanced anomaly detection. The resulting approach achieves fully unsupervised anomaly detection in surveillance video. The experimental results show improved detection performance compared with the state-of-the-art methods.
Inspec keywords: belief networks; video signal processing; particle filtering (numerical methods); Kalman filters; video surveillance
Other keywords:
Subjects: Filtering methods in signal processing; Combinatorial mathematics; Combinatorial mathematics; Video signal processing; Image recognition
References
-
-
1)
- Kratz, L., Nishino, K.: `Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models', IEEE Conf. Computer Vision and Pattern Recognition, June 2009, Miami, FL, p. 1446–1453.
-
2)
- C. Piciarelli , C. Micheloni , G.L. Foresti . Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. , 11 , 1544 - 1554
-
3)
- T. Xiang , S. Gong . Spectral clustering with eigenvector selection. Patt. Recogn. , 3 , 1012 - 1029
-
4)
- W. Hu , X. Xiao , Z. Fu , D. Xie , T. Tan , S. Maybank . A system for learning statistical motion patterns. IEEE Trans. Patt. Anal. Mach. Intell. , 9 , 1450 - 1464
-
5)
- A. Yilmaz , O. Javed , M. Shah . Object tracking: a survey. ACM Comput. Surv. , 4 , 1 - 45
-
6)
- C.-C. Chang , T.-L. Chia , C.-K. Yang . Modified temporal difference method for change detection. Opt. Eng. , 2 , 027001 - 027001
-
7)
- A.Y. Ng , M.I. Jordan , Y. Weiss . On spectral clustering: analysis and an algorithm. Adv. Neural Inf. Process. Syst. , 849 - 856
-
8)
- D. Comaniciu , V. Ramesh , P. Meer . Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. , 564 - 577
-
9)
- C. Rasmussen , G. Hager . Probabilistic data association methods for tracking complex visual objects. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 560 - 576
-
10)
- A. Elgammal , R. Duraiswani , D. Harwood , L.S. Davis . Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE , 7 , 1151 - 1163
-
11)
- I. Haritaoglu , D. Hartwood , L.S. Davis . Real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. , 809 - 830
-
12)
- M.S. Arulampalam , S. Maskell , N. Gordon , T. Clapp . A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. , 2 , 174 - 188
-
13)
- C. Stauffer , W. Grimson . Learning patterns of activity using real time tracking. IEEE Trans. Pattern Anal. Mach. Intell. , 8 , 747 - 757
-
14)
- Zivkovic, Z.: `Improved adaptive Gaussian mixture model for background subtraction', Proc. 17th Int. Conf. Pattern Recognition, August 2004, p. 28–31.
-
15)
- K. Ball , D. Lyon , D.M. Wood , C. Norris , C. Raab . A report on the surveillance society.
-
16)
- E. Maggio , M. Taj , A. Cavallaro . Effcient multitarget visual tracking using random finite sets. IEEE Trans. Circuits Syst. Video Technol. , 8 , 1016 - 1027
-
17)
- H. Li , D.R. Bull , A. Achim . (2010) Unsupervised feature based abnormality detection.
-
18)
- H. Bhaskar , L. Mihaylova , A. Achim . Video foreground detection based on symmetric alpha-stable mixture models. IEEE Trans. on Circuits and Systems for Video Technology , 8 , 1133 - 1138
-
19)
- Beymer, D., Konolige, K.: `Real-time tracking of multiple people using continuous detection', IEEE Int. Conf. Computer Vision Frame-Rate Workshop, 1999.
-
20)
- K. Nummiaro , E. Koller-Meierb , L.V. Gool . An adaptive colour-based particle filter. Image Vis. Comput. , 99 - 110
-
21)
- Wiliem, A., Madasu, V., Boles, W., Varlagadda, P.: `Detecting uncommon trajectories', Computing: Techniques and Applications, December 2008, Canberra, ACT, p. 398–404.
-
22)
- T. Xiang , S. Gong . Video behavior profiling for anomaly detection. IEEE Trans. Patt. Anal. Mach. Intell. , 5 , 893 - 908
-
23)
- N. Johnson , D. Hogg . Learning the distribution of object trajectories for event recognition. Image Vis. Comput. , 8 , 609 - 615
-
24)
- N. Otsu . A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cyber. , 62 - 66
-
25)
- L. Zelnik-Manor , P. Perona . Self-tuning spectral clustering. Adv. Neural Inf. Process. Syst. , 1601 - 1608
-
26)
- X. Wang , X. Ma , W.E. Grimson . Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. IEEE Trans. Pattern Anal. Mach. Intell. , 3 , 539 - 555
-
27)
- Stauffer, C., Grimson, W.E.L.: `Adaptive background mixture models for real-time tracking', Computer Society Conf. Computer Vision and Pattern Recognition, 1999, Fort Collins, CO, p. 246–252.
-
28)
- L. Li , W. Huang , I.Y.-H. Gu , Q. Tian . Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans. Signal Process. , 11 , 1459 - 1472
-
29)
- R.E. Kalman . A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. , 35 - 45
-
30)
- Porikli, F., Haga, T.: `Event detection by eigenvector decomposition using object and frame features', IEEE Computer Society Conf. Computer Vision and Pattern Recognition Workshops, June 2004, p. 114–121.
-
31)
- Y. Bar-Shalom , T.E. Fortmann . (1988) Tracking and data association.
-
32)
- R. Jonker , A. Volgenant . A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing , 325 - 340
-
33)
- Mehran, R., Oyama, A., Shah, M.: `Abnormal crowd behavior detection using social force model', IEEE Conf. Computer Vision and Pattern Recognition, June 2009, Miami, FL, p. 935–942.
-
34)
- Horprasert, T., Harwood, D., Davis, L.S.: `A statistical approach for real-time robust background subtraction and shadow detection', IEEE Frame-Rate Applications Workshop, 1999, Kerkyra, Greece.
-
35)
- Cai, Y., Freitas, N., Little, J.J.: `Robust visual tracking for multiple targets', Ninth European Conf. Computer Vision, May 2006, Graz, Austria, p. 107–118.
-
36)
- T. Xiang , S. Gong . Incremental and adaptive abnormal behaviour detection. Comput. Vis. Image Understand. , 1 , 59 - 73
-
37)
- Friedman, N., Russell, S.: `Image segmentation in video sequences: a probabilistic approach', 13thConf. Uncertainty in Artificial Intelligence, 1997, San Francisco, CA, p. 175–181.
-
38)
- Bashir, F.I., Khokhar, A.A., Schonfeld, D.: `Segmented trajectory based indexing and retrieval of video data', Proc. 2003 Int. Conf. Image Processing, September 2003, p. 623–626.
-
39)
- Li, H., Achim, A., Bull, D.R.: `GMM-based efficient foreground detection with adaptive region update', IEEE Int. Conf. Image Processing, November 2009, Cairo, p. 3181–3184.
-
40)
- Collins, R., Lipton, A., Kanade, T.: `A system for video surveillance and monitoring', Proc. American Nuclear Society Eighth Internal Topical Meeting on Robotics and Remote Systems, 1999.
-
1)
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