TA-NET: Empowering Highly Efficient Traffic Anomaly Detection Through Multi-Head Local Self-Attention and Adaptive Hierarchical Feature Reconstruction

Junzhou Chen, Jiajun Pu, Baiqiao Yin, Ronghui Zhang, Jun Jie Wu

Abstract: In the realm of road surveillance systems, Automatic Incident Detection (AID) methods have shown promise in swiftly and precisely detecting traffic anomalies. Nevertheless, the paucity of frame-level precisely annotated training data poses substantial challenges. To navigate this issue, we introduce TA-NET, a novel framework designed to highly efficient traffic anomaly detection. TA-NET utilizes a generic dataset pre-training model to facilitate weakly supervised learning. It comprises two main modules: the Adaptive Hierarchical Feature Reconstruction Block (AHFRB) and the Multi-Head Local Self-Attention (MHLSA) mechanism. AHFRB refines the feature extractor by reconstructing the features drawn from the pre-trained model. Concurrently, MHLSA scrutinizes the contextual relationships between contiguous video segments and bolsters anomaly detection accuracy. Our approach was validated using the TAD testing dataset, with the results highlighting the efficacy of TA-NET. Remarkably, it accomplishes an Area Under the Curve (AUC) of 94.47% for the overall dataset and 70.78% for the anomaly subset. These scores surpass the previous state-of-the-art method by 1.54% and 4.96%, respectively, attesting to TA-NET’s superior performance. Consequently, our study offers a fresh perspective and sets a new standard for future video-based traffic incident detection research.