Mutuality Attribute Makes Better Video Anomaly Detection

Abstract

Video anomaly detection (VAD) is an essential but challenging task. Existing prevalent methods focus on analyzing the reconstruction or prediction difference between normal and abnormal patterns through multiple deep features, e.g., optic flow. However, these approaches independently use deep features to characterize attributes, ignore the mutuality among multiple deep features. Therefore, the constructed representation is limited to indirectly representing the anomaly from isolated attributes, and makes the network difficult to capture the high-level causes of anomaly. In this paper, we proposed a novel Mutuality Attribute-based Representation framework (MAR-VAD) for the VAD task, which absorbs the mutuality among deep features to characterize the mutuality attribute. Specifically, the mutuality attribute encapsulates high-level semantic information, such as the specific abnormal object or action, which mutually utilizes information from multiple deep features. In this way, the system is able to directly capture the high-level causes of anomaly, thus providing a more comprehensive perspective to accurately detect anomaly events. Following a process-transparent density estimation, we produce the final anomaly scores. Experiments show that MAR-VAD achieves state-of-the-art performance on ShanghaiTech and Avenue.