构建复杂视频异常检测数据集并用时空图建模物体交互
ComplexVAD: Detecting Interaction Anomalies in Video
- 用带时空属性的场景图建模物体间交互关系
- 在新数据集ComplexVAD上实现优于现有方法的检测性能
- 适合研究视频中复杂行为异常的学者使用
现有视频异常检测数据集难以体现因物体间交互导致的复杂异常，导致研究多集中于简单异常。为解决此问题，我们提出一个大规模新数据集ComplexVAD。同时，提出一种新方法，通过带有时空属性的场景图来建模物体间的交互关系。在ComplexVAD上，结合该方法与其他两种先进方法，建立基线性能，并验证所提方法优于现有工作。
原文摘要 · Abstract (English)
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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