Structured Graph Reasoning for Traffic Anomaly Detection

Unsupervised traffic anomaly detection aims to detect abnormal frames and objects in dashcam videos given only normal videos during training. Most prior works focus on reconstruction or prediction pipelines, while recent graph-based extensions still rely on graph feature learning or limited statistical interactions. Consequently, reasoning over structured relations and abnormal interactions remains largely unexplored. This paper introduces a structured graph reasoning method that bridges normality learning, anomaly detection, and structured reasoning in a unified framework. Unlike prior works, our approach interprets the graph as a semantic abstraction tailored for comprehensive traffic scene understanding. It includes three innovative components: (1) A domain-specific graph representation that models semantic relationships for road scenarios, providing a holistic description of traffic activities. (2) A graph reasoning detector that detects traffic anomalies by reasoning over structured traffic scene graphs. It integrates node anomaly, i.e., unusual object movements, and edge anomaly, i.e., abnormal object interactions, within a unified graph optimization approach, enabling the effective detection of complex, interaction-driven anomalies. (3) An Anomaly Transformer that predicts future traffic scene graphs by attending node and edge queries to object locations and relation histories, thereby effectively learning individual and interaction-level normal traffic behaviors. Experiments on two public datasets, i.e., DoTA and MM-AU, demonstrate the superior performance of our method and its effectiveness in detecting complex abnormal traffic events.