Low rank sparsity prior for robust video anomaly detection

Xuan Mo, Vishal Monga, Raja Baia, Zhigang Fan, Aaron Burry

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations

    Abstract

    Recently, sparsity based classification has been applied to video anomaly detection. A linear model is assumed over video features (e.g. trajectories) such that the feature representation of a new event is written as a sparse linear combination of existing feature representations in the dictionary. Sparsity based video anomaly detection shows promise but open challenges remain in that existing methods assume object specific and class specific event dictionaries making them applicable mostly in highly structured scenarios. Second, using conventional sparsity models on matrices/vectors, the computational burden is often high. In this work, we advocate a more general and practical sparsity model using a low-rank structure on the matrix of sparse coefficients. We find that enforcing a low-rank structure can ease the rigidity of traditional row-sparse constraints on sparse coefficient vectors/matrices. Because low-rank matrices are of course not always sparse, an additional l1 regularization term is added. Further, if rank is substituted by its convex nuclear norm alternative, then significant computational benefits can be obtained over existing methods in sparsity based video anomaly detection. Experimental evaluation on benchmark video datasets reveal, our method is competitive with state-of-the art while providing robustness benefits under occlusion.

    Original languageEnglish (US)
    Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1285-1289
    Number of pages5
    ISBN (Print)9781479928927
    DOIs
    StatePublished - Jan 1 2014
    Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
    Duration: May 4 2014May 9 2014

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    ISSN (Print)1520-6149

    Other

    Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
    CountryItaly
    CityFlorence
    Period5/4/145/9/14

    All Science Journal Classification (ASJC) codes

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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    Mo, X., Monga, V., Baia, R., Fan, Z., & Burry, A. (2014). Low rank sparsity prior for robust video anomaly detection. In 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 (pp. 1285-1289). [6853804] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2014.6853804