Practical methods for sparsity based video anomaly detection

Xuan Mo, Vishal Monga, Raja Bala, Jose A. Rodrguez-Serrano, Zhigang Fan, Aaron Burry

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

    Abstract

    Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. Recently, sparse reconstruction techniques have been used for image classification, and shown to provide excellent robustness to occlusion. This progress has also been leveraged for sparsity based video anomaly detection where test trajectories are expressed as sparse linear combinations of example trajectories from a given (normal or anomalous) class. While sparsity based anomaly detection techniques are promising, they pose practical challenges due to their increased computational burden and the need for generous manually labeled training (even if only for normal event trajectories). Our work focuses on overcoming these limitations. Our central contribution is a dictionary design and optimization technique that can effectively reduce the size of training dictionaries that enable sparsity based classification/anomaly detection without adversely influencing detection performance. We also suggest the use of state of the art automatic trajectory clustering techniques for initializing dictionaries which can alleviate the burden of manual labeling. Experimental results show that significant computational advantages can be obtained with the proposed techniques with little performance loss over using large and manually labeled dictionaries of example trajectories.

    Original languageEnglish (US)
    Title of host publication2013 16th International IEEE Conference on Intelligent Transportation Systems
    Subtitle of host publicationIntelligent Transportation Systems for All Modes, ITSC 2013
    Pages955-960
    Number of pages6
    DOIs
    StatePublished - Dec 1 2013
    Event2013 16th International IEEE Conference on Intelligent Transportation Systems: Intelligent Transportation Systems for All Modes, ITSC 2013 - The Hague, Netherlands
    Duration: Oct 6 2013Oct 9 2013

    Other

    Other2013 16th International IEEE Conference on Intelligent Transportation Systems: Intelligent Transportation Systems for All Modes, ITSC 2013
    CountryNetherlands
    CityThe Hague
    Period10/6/1310/9/13

    All Science Journal Classification (ASJC) codes

    • Automotive Engineering
    • Mechanical Engineering
    • Computer Science Applications

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  • Cite this

    Mo, X., Monga, V., Bala, R., Rodrguez-Serrano, J. A., Fan, Z., & Burry, A. (2013). Practical methods for sparsity based video anomaly detection. In 2013 16th International IEEE Conference on Intelligent Transportation Systems: Intelligent Transportation Systems for All Modes, ITSC 2013 (pp. 955-960). [6728355] https://doi.org/10.1109/ITSC.2013.6728355