Improved anomaly detection in surveillance videos with multiple probabilistic models inference

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 activ-ities, 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 sequences of practical surveillance scenarios. However, there is a lack of video anomaly detection methods suitable for real-time processing, and the trade-off between detection accuracy and computational complexity has not been given much attention. To reduce high computational complexity and shorten frame processing times, we employ a variable-sized cell structure and extract a compact feature set from a limited number of video volumes during the feature extraction stage. In conclusion, we propose a real-time video anomaly detection algorithm called MPI-VAD that combines the advantages of multiple probabilistic models inference. Experiment results on three publicly available datasets show that the proposed method attains competitive detection accuracies and superior frame processing speed.