ComplexVAD: Detecting Interaction Anomalies in Video

• URL: http://arxiv.org/abs/2501.09733v1
• Date: Thu, 16 Jan 2025 18:35:45 GMT
• Title: ComplexVAD: Detecting Interaction Anomalies in Video
• Authors: Furkan Mumcu, Michael J. Jones, Yasin Yilmaz, Anoop Cherian,
• Abstract summary: We introduce a new large-scale anomaly detection dataset: ComplexVAD. In addition, we propose a method to detect complex anomalies via modeling interactions between objects using a scene graph with video 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.
• Score: 45.08126325125808
• License:
• 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 baseline scores on ComplexVAD and demonstrate that our new method outperforms existing works.

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