Anomaly detection based on maximum a posteriori

•A novel framework using maximum a posteriori for anomaly detection is proposed.•The prior knowledge for anomaly detection is integrated within the Baysian framework.•A maximum grid template is designed for computing the likelihood function. In this paper, we propose a novel method to detect abnormal events from videos based on a maximum a posteriori (MAP). Conventional methods consider the events with low-probability with respect to a model of normal behavior as anomaly. Different from the traditional approaches, the anomaly detection is achieved by a MAP estimation in our framework. The prior knowledge is obtained from the background subtraction due to the fact that the anomalies often occur at the locations consisting of moving objects, and the likelihood function is computed by comparing the similarity between the testing samples and a designed maximum grid template. Experiments on three public databases show that our method can effectively detect abnormal events in complex scenes.