Fast detection of abnormal events in videos with binary features

Roberto Leyva, Victor Sanchez, Chang Tsun Li

    Research output: Book chapter/Published conference paperConference paperpeer-review

    8 Citations (Scopus)


    Millions of surveillance cameras are currently installed in public places around the world, making it necessary to intelligently analyse the acquired data to detect the occurrence of abnormal events. A vast number of methods to detect such events have been recently proposed; unfortunately, there is a lack of methods capable of detecting these events as frames are acquired, also known as online processing. In this paper, we present an online framework for video anomaly detection that employs binary features to encode motion information, and low-complexity probabilistic models for detection. Evaluation results on the popular UCSD dataset and on a recently introduced real-event video surveillance dataset show that our framework outperforms non-online and online methods.
    Original languageEnglish
    Title of host publication2018 IEEE International conference on acoustics, speech, and signal processing (ICASSP)
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages5
    EditionApril 2018
    ISBN (Electronic)9781538646588
    ISBN (Print)9781538646588
    Publication statusPublished - 13 Sept 2018
    Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2018 - Calgary Telus Convention Center, Calgary, Canada
    Duration: 15 Apr 201820 Apr 2018 (Conference website)


    Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing
    Abbreviated titleSignal processing and artificial intelligence: Changing the world
    Internet address


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