Abstract:

Existing video anomaly detection datasets are inadequate for representing complex anomalies that occur due to the interactions between objects. The absence of complex anomalies in previous video anomaly detection datasets affects research by shifting the focus onto simple anomalies. To ad- dress this problem, we introduce a new large-scale dataset: ComplexVAD. In addition, we propose a novel method to detect complex anomalies via modeling the interactions be- tween objects using a scene graph with spatio-temporal attributes. With our proposed method and two other state- of-the-art video anomaly detection methods, we obtain base- line scores on ComplexVAD and demonstrate that our new method outperforms existing works.