ComplexVAD: Detecting Interaction Anomalies in Video

Furkan Mumcu, Michael J. Jones, Yasin Yilmaz, Anoop Cherian

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 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 base-line scores on ComplexVAD and demonstrate that our new method outperforms existing works.

External IDs:dblp:conf/wacv/Mumcu0YC25