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    Improved Anomaly Detection in Surveillance Videos with Multiple Probabilistic Models Inference

    Zhen Xu1, Xiaoqian Zeng1, Genlin Ji1,*, Bo Sheng2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1703-1717, 2022, DOI:10.32604/iasc.2022.016919

    Abstract Anomaly detection in surveillance videos is an extremely challenging task due to the ambiguous definitions for abnormality. In a complex surveillance scenario, the kinds of abnormal events are numerous and might co-exist, including such as appearance and motion anomaly of objects, long-term abnormal activities, etc. Traditional video anomaly detection methods cannot detect all these kinds of abnormal events. Hence, we utilize multiple probabilistic models inference to detect as many different kinds of abnormal events as possible. To depict realistic events in a scene, the parameters of our methods are tailored to the characteristics of video… More >

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