Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos

arXiv:2401.03522v2 Announce Type: replace
Abstract: Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of autonomous driving and advanced driver assistance systems. Previous single-stage TAD methods primarily rely on frame prediction, making them vulnerable to interference from dynamic backgrounds induced by the rapid movement of the dashboard camera. While two-stage TAD methods appear to be a natural solution to mitigate such interference by pre-extracting background-independent features (such as bounding boxes and optical flow) using perceptual algorithms, …

abstract advanced advanced driver assistance anomaly anomaly detection arxiv autonomous autonomous driving cs.cv detection driver driving dynamic interference making modeling prediction safety stage systems temporal text them traffic type videos vulnerable