Anomalous Human Activity Detection Based on Online One-Class SVM with N-Grams Kernel

Abstract:

We present a novel online unsupervised anomaly detection method for human activities. The proposed approach is based on one-class support vector machine (OCSVM) clustering, where the novelty detection SVM capabilities are used for the identification of anomalous activities. Particular attention is given to activity classification in absence of a priori information on the distribution of outliers. Activities are represented by variable-length event sequences, but the most commonly used kernels are defined on fixed-dimension spaces. To solve the problem, we develop a novel sequence-similarity kernel, the n-grams kernel. Our kernel is conceptually simple and efficient to compute and performs well in comparison with state-of-the-art methods for anomaly detection. Moreover, most SVM algorithms require large number of memory to store the kernel matrix, or repeated access to the training samples. This makes it infeasible for online anomaly detection. In this paper, we develop simple and computationally efficient online learning algorithms for anomaly detection.

• Periodical: Advanced Materials Research (Volumes 143-144)
• Pages: 648-652
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.143-144.648
• Online since: October 2010
• Authors: Xu Dong Zhu, Zhi Jing Liu
• Keywords: Anomaly Detection, Computer Vision, Fisher Kernel, One-Class Support Vector Machine

References

[1] A. Yilmaz, and M. Shah, Recognizing human actions in videos acquired by un-calibrated moving cameras, Proc. the tenth IEEE international conference on computer vision, Beijing, China, pp.150-157, (2005).

DOI: 10.1109/iccv.2005.201

[2] Y. Song, L. Goncalves, and P. Perona, Unsupervised learning of human motion, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 25, pp.1-14, (2003).

DOI: 10.1109/tpami.2003.1206511

[3] R. Hamid, S. Maddi, A. Johnson, A. Bobick, I. Essa, C. Isbell, A novel sequence representation for unsupervised analysis of human activities, Arti¯cial Intelligence Journal, vol. 173, no. 14, pp.1221-1244, (2009).

DOI: 10.1016/j.artint.2009.05.002

[4] C. Piciarelli, C. Micheloni, G.L. Foresti, Trajectory-Based Anomalous Event Detection, IEEE Trans. circuits and systems for video technology, vol. 18, no. 11, pp.1544-1554, (2008).

DOI: 10.1109/tcsvt.2008.2005599

[5] J. Kivinen, A.J. Smola, R.C. Williamson, Online learning with kernels, IEEE Trans. Signal Processing, vol. 52, no 8, pp: 2165-2176, (2004).

DOI: 10.1109/tsp.2004.830991

[6] .

[6] D. Haussler, Convolution kernels on discrete structure, Technical report, UC Santa Cruz, (1999).

[7] C. Watkins, Dynamic alignment kernels, Technical report, UL Royal Holloway, (1999).

[8] Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, and ChrisWatkins, Text classification using string kernels, Journal of Machine Learning Research, vol 2, pp. 419C444, (2002).

[9] T. Hofmann, Learning the Similarity of Documents: An Information Geometric Approach to Document Retrieval and Categorization, Advances in Neural Information Processing System, vol. 12, pp.914-920, (2000).

[10] C. Schuldt, I. Laptev, B. Caputo, Recognizing human actions: a local svm approach, ICPR, pp.32-36, (2004).

DOI: 10.1109/icpr.2004.1334462

[11] M. Blank, L. Gorelick, E. Shechtman, M. Irani, R. Basri, Actions as space-time shapes, Proc. The tenth IEEE international conference on computer vision Los Alamitos, pp.1395-1402, (2005).

DOI: 10.1109/iccv.2005.28