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Sparse Representation based Anomaly Detection using HOMV in H.264 Compressed Videos

Biswas, Sovan and Babu, Venkatesh R (2014) Sparse Representation based Anomaly Detection using HOMV in H.264 Compressed Videos. In: International Conference on Signal Processing and Communications (SPCOM), JUL 22-25, 2014.

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Abstract

In this paper, we have proposed an anomaly detection algorithm based on Histogram of Oriented Motion Vectors (HOMV) 1] in sparse representation framework. Usual behavior is learned at each location by sparsely representing the HOMVs over learnt normal feature bases obtained using an online dictionary learning algorithm. In the end, anomaly is detected based on the likelihood of the occurrence of sparse coefficients at that location. The proposed approach is found to be robust compared to existing methods as demonstrated in the experiments on UCSD Ped1 and UCSD Ped2 datasets.

Item Type: Conference Proceedings
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Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords: Anomaly detection; Histogram of Oriented Motion Vectors; Sparse representation
Department/Centre: Division of Information Sciences > Supercomputer Education & Research Centre
Date Deposited: 30 Dec 2015 06:07
Last Modified: 30 Dec 2015 06:07
URI: http://eprints.iisc.ernet.in/id/eprint/52972

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