Video Anomaly Prediction: Problem, Dataset and Method

The task of Video Anomaly Detection (VAD) is to find anomalies existing in given videos, which has been extensively studied. This paper addresses the task of Video Anomaly Prediction (VAP), which is to predict whether any anomaly will happen in streaming videos. With VAP, we can intervene or alert before anomalies really occur, thus preventing damage to public life and property. VAP is a more significant yet challenging task, which currently has only one work in the literature. To challenge this task, we first propose the concepts of predictable and unpredictable anomalies, and define the VAP task in video surveillance scenario. Based on this definition, we then construct the first VAP dataset, which consists of 618 frame-level annotated videos, including 300 normal videos and 318 anomaly videos. Next, we propose a Dual-channel Video Anomaly Prediction (DVAP) method based on feature enhancement and a new annotation scheme. Finally, We evaluate the proposed method and compare it with related works from classification and regression perspectives. Experimental results show that DVAP can predict anomalies one second earlier than they really occur and achieves the best accuracy.