Dual-Stream Anomaly Detection Network for Real-World Traffic Scenarios

In the realm of video analytics, the significance of video anomaly detection cannot be ignored. Many video anomaly detection methods have been introduced in traffic scenarios, and they have achieved some promising results. However, there still exists a large room for improving the traffic anomaly detection performance. In this paper, we present an end-to-end approach, the Dual-Stream Anomaly Detection Network (DS-ADN), specialized for traffic anomaly detection. DS-ADN introduces optical flow data and utilizes inter-flow information interaction coupled with the Multi-Scale Attention Fusion Module (MSA-FM) to comprehensively merge RGB and optical flow data. It features an innovative depth-based prediction loss function that adapts to the vehicle’s regular shape and events at different distances under the surveillance camera. This adaptation occurs through block-wise average pooling and weighted allocation across varying depths. Considering the prevalent issues of datasets dedicated to traffic anomaly detection generally containing a single type of anomaly, this paper introduces the Traffic Surveillance Dataset (TSD) containing multiple anomalies for model validation. Extensive experiments on the TSD dataset highlight the better performance of our DS-ADN, manifesting an AUC gain of over 8.8% compared to other state-of-the-art models. Moreover, DS-ADN achieves competitive performance across three widely-used anomaly detection datasets that contain multiple anomalies, including UCSD Ped2, Avenue, and ShanghaiTech, demonstrating its generalization ability.