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2011 | OriginalPaper | Buchkapitel

Learning Behavioral Patterns of Time Series for Video-Surveillance

verfasst von : Nicoletta Noceti, Matteo Santoro, Francesca Odone

Erschienen in: Machine Learning for Vision-Based Motion Analysis

Verlag: Springer London

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Abstract

This chapter deals with the problem of learning behaviors of people activities from (possibly big) sets of visual dynamic data, with a specific reference to video-surveillance applications. The study focuses mainly on devising meaningful data abstractions able to capture the intrinsic nature of the available data, and applying similarity measures appropriate to the specific representations. The methods are selected among the most promising techniques available in the literature and include classical curve fitting, string-based approaches, and hidden Markov models. The analysis considers both supervised and unsupervised settings and is based on a set of loosely labeled data acquired by a real video-surveillance system. The experiments highlight different peculiarities of the methods taken into consideration, and the final discussion guides the reader towards the most appropriate choice for a given scenario.

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Vorheriges Kapitel Spatio-Temporal Motion Pattern Models of Extremely Crowded Scenes

Nächstes Kapitel Recognition of Spatiotemporal Gestures in Sign Language Using Gesture Threshold HMMs

Note that in the unsupervised setting, the similarity function plays the role of the kernel function of the RLS case—as they both model the similarity among available data.

Available for downloading at http://people.cs.ubc.ca/~murphyk/Software/BNT/bnt.html.

Further details on: http://www.mathworks.com/products/splines/.

The Imanalysis suite, we obtained within a technology transfer program with the company Imavis srl, http://www.imavis.com/.

Anjum, N., Cavallaro, A.: Multifeature object trajectory clustering for video analysis. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1555–1564 (2008) CrossRef

Bashir, F., Khokhar, A., Schonfeld, D.: Object trajectory-based activity classification and recognition using hidden Markov model. IEEE Trans. Image Process. 16, 1912–1919 (2007) MathSciNetCrossRef

Bolshakova, N., Azuaje, F.: Cluster validation techniques for genome expression data. Signal Process. 83(4), 825–833 (2003) MATHCrossRef

Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003) CrossRef

Doretto, G., Chiuso, A., Wu, Y., Soatto, S.: Dynamic textures. Int. J. Comput. Vis. 51(2), 91–109 (2003) MATHCrossRef

Fowlkes, C., Belongie, S., Chung, F.R.K., Malik, J.: Spectral grouping using the Nyström method. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 214–225 (2004) CrossRef

Günter, S., Bunke, H.: Validation indices for graph clustering. Pattern Recogn. Lett. 24(8), 1107–1113 (2003) MATHCrossRef

Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17, 107–145 (2001) MATHCrossRef

Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., Maybank, S.: A system for learning statistical motion patterns. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1450–1464 (2006) CrossRef

10.

Ivanov, Y., Bobick, A.: Recognition of visual activities and interactions by stochastic parsing. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 852–872 (2000) CrossRef

11.

Fei-Fei, L., Niebles, J.C., Wang, H.: Unsupervised learning of human action categories using spatial-temporal words. In: Proc. of BMVC (2006)

12.

Jebara, T., Kondor, R.I., Howard, A.: Probability product kernels. J. Mach. Learn. Res. 5, 819–844 (2004) MathSciNetMATH

13.

Jebara, T., Song, Y., Thadani, K.: Spectral clustering and embedding with hidden Markov models. In: Kok, J.N., Koronacki, J., López de Mántaras, R., Matwin, S., Mladenic, D., Skowron, A. (eds.) ECML. Lecture Notes in Computer Science, vol. 4701, pp. 164–175. Springer, Berlin (2007)

14.

Liao, T.W.: Clustering of time series data: a survey. Pattern Recogn. 38(11) (2005)

15.

Lin, W., Orgun, M.A., Williams, G.J.: Temporal data mining using multilevel-local polynomial models. In: International Conference IDEAL. Lecture Notes in Computer Science, vol. 1983. Springer, Berlin (2000)

16.

Lo Gerfo, L., Rosasco, L., Odone, F., De Vito, E., Verri, A.: Spectral algorithms for supervised learning. Neural Comput. 20, 1873–1897 (2008) MathSciNetMATHCrossRef

17.

Medioni, G., Cohen, I., Bremond, F., Hongeng, S., Nevatia, R.: Event detection and analysis from video streams. IEEE Trans. Pattern Anal. Mach. Intell. 23, 873–889 (2001) CrossRef

18.

Morris, B., Trivedi, M.M.: Learning trajectory patterns by clustering: experimental studies and comparative evaluation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’09 (2009)

19.

Noceti, N., Santoro, M., Odone, F.: String-based spectral clustering for understanding human behaviours. In: THEMIS-BMVC (2008)

20.

Noceti, N., Santoro, M., Odone, F.: Unsupervised learning of behavioural patterns for video-surveillance. In: Workshop MLVMA-ECCV (2008)

21.

Noceti, N., Destrero, A., Lovato, A., Odone, F.: Combined motion and appearance models for robust object tracking in real-time. In: AVSS (2009)

22.

Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1544–1554 (2008) CrossRef

23.

Pittore, M., Campani, M., Verri, A.: Learning to recognize visual dynamic events from examples. In: IJCV (2000)

24.

Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989) CrossRef

25.

Rieck, K., Laskov, P.: Linear-time computation of similarity measures for sequential data. J. Mach. Learn. Res. 9, 23–48 (2008) MATH

26.

Shawe Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004) CrossRef

27.

Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000) CrossRef

28.

Special issue on event analysis in videos. IEEE Trans. Circuits Syst. Video Technol. 18(11) (2008)

29.

Stauffer, C., Grimson, E.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Recogn. Mach. Intell. 22(8), 747–757 (2000) CrossRef

30.

Tommasi, T., Orabona, F., Caputo, B.: Discriminative cue integration for medical image annotation. Pattern Recognit. Lett. 29(15), 1996–2002 (2008) CrossRef

31.

Vishwanathan, S.V., Smola, A.J., Vidal, R.: Binet–Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes. Int. J. Comput. Vis. 73(1), 95–119 (2007) CrossRef

Titel: Learning Behavioral Patterns of Time Series for Video-Surveillance
verfasst von: Nicoletta Noceti
Matteo Santoro
Francesca Odone
Verlag: Springer London
Buch: Machine Learning for Vision-Based Motion Analysis
Print ISBN: 978-0-85729-056-4

Electronic ISBN: 978-0-85729-057-1

Copyright-Jahr: 2011
DOI: https://doi.org/10.1007/978-0-85729-057-1_11

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