Retrieving and Reasoning: Multivariate Feature and Attribute Cooperation for Video Anomaly Detection

Video anomaly detection (VAD), which detects abnormal patterns in video sequence, is based on several kinds of features or attributes in the existing methods. This ignores the interconnections between different features and attributes, and the initiation of an anomalous result is brought about by multiple factors. If several individual neural networks are used to perceive various types of anomalies, the system would lose awareness of the association among features and attributes, which limits the system's ability to perceive complex anomalies. In this work, we propose a dual-branch framework for VAD task, which includes deep feature retrieving and semantic attribute reasoning branch. In the former branch, three high-dimensional deep features are extracted and modeled, then the anomaly scores are obtained based on the vector retrieval database. In the latter branch, three low-dimensional semantic-level attributes are extracted for composing the attribute triplets, then use the Association-rule Mining Module (AMM) to perceive potential connections among these triplets. The coefficients computed by the latter branch calibrate the anomaly scores obtained by the former while providing high-level anomaly causes. Extensive experiments show that our approach achieves state-of-the-art performance with 87.9$\%$ on ShanghaiTech and 94.6$\%$ on Avenue.