ComplexVAD: Detecting Interaction Anomalies in Video

Furkan Mumcu, Michael Jones, Yasin Yilmaz, Anoop Cherian; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1093-1102

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 address 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 between 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 baseline scores on ComplexVAD and demonstrate that our new method outperforms existing works.