An Interaction-Scene Collaborative Representation Framework for Detecting Traffic Anomalies in Driving Videos

Accurate traffic anomaly detection (TAD) is critical for intelligent transportation systems. Previous TAD methods mainly relied on driving scene perception or motion patterns of agents (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i>, traffic participants, such as vehicles and pedestrians) to detect traffic anomalies. Although these methods achieve promising detection performance, they lack an intuitive modeling of agent interactions, which limits their ability to handle complex driving scenarios. In fact, modeling interactions among agents helps to understand the underlying logic behind changes in agent behavior, thereby benefiting traffic anomaly detection. In our work, we introduce Interaction-Scene Collaborative Representation for Traffic Anomaly Detection (ISCRTAD), an innovative framework that leverages advanced artificial intelligence techniques to model agent interactions in dynamic driving scenarios. Unlike previous TAD methods, the proposed approach is the first try to collaboratively represent agent interactions and dynamic driving scenarios, significantly enhancing the perception and understanding of traffic anomalies in driving videos. First, we introduce the agent interaction modeling module, which comprehensively models the interactions between agents in driving scenarios through the designed Behavior Interaction Graph and Spatial Perception Graph. Furthermore, we design a heterogeneous modality collaborative representation (HMCR) mechanism to deeply integrate agent interactions with dynamic driving scenarios, thereby enabling a more profound understanding of agent motion patterns in dynamic driving environments. Experimental results on the DoTA and DADA datasets demonstrate significant improvements in traffic anomaly detection performance, highlighting the effectiveness of our AI-driven approach.