Specific event detection for video surveillance using variational Bayesian inference

Although in recent years there has been a trend to incorporate data-driven models to automate the task of video surveillance, there remains a shortage of learning-based solutions for the detection of specific events that do not require examples of such events during training. Current research largely focuses on abnormal event detection, a learning-based approach that allows for the detection of events as anomalies. Unfortunately, such an approach does not provide any details about the types of events detected as the training data only includes normal events. This paper proposes an approach to detect specific events in surveillance videos under an anomaly detection framework by constraining the input space of the detection model, and hence, allowing to determine the type of event detected as an anomaly. Specifically, the proposed approach exploits the benefits of variational Bayesian inference to build probabilistic models for the detection of specific events as anomalies. Such a novel strategy leverages the benefits of the learning mechanism used by abnormal event detection frameworks to detect specific events. The proposed approach achieves a very competitive performance for the detection of several events within the context of video surveillance in public transport stations. It outperforms the state-of-the-art for the detection of pieces of abandoned luggage.