Selection Biased Positive and Unlabeled Learning Method for Anomaly Detection in Surveillance Videos

Anomaly detection in surveillance videos aims at identifying abnormal event under specific scenarios and it is widely applied in public security, smart city, and pedestrian surveillance. In the weakly-supervised setting, most existing anomaly detection approaches are formulated as the classic multiple-instance learning problem. In this paper, we provide a unique perspective that selection biased positive and unlabeled learning. In such a viewpoint, as long as estimating the label frequency from training set, we can effectively apply supervised classifier to weakly supervised anomaly detection, and take greater advantage of these well-developed classifiers. For this purpose, we present a novel method to estimate label frequency from the attribute subdomains with large label probability. In the test phase, we only use the label frequency to modify the supervised classifier. Comprehensive experiments a re performed on different scales datasets. Our method provides superior on all dataset which demonstrate the effectiveness.