Temporal-Spatial Decoupled Self-Supervised Multi-Task Learning for Video Anomaly Detection and Localization in Intelligent Transportation Surveillance Systems

Video anomaly detection (VAD) has become a critical research focus in the realms of computer vision and intelligent transportation systems, particularly with the rise of generative AI and advancements in self-supervised learning. These developments open up new frontiers in understanding complex scenarios within intelligent transportation systems. Since previous methods, confined to a single paradigm, inadequately captured intricate feature information, which led to a bottleneck that constrained the performance; individuals form unbiased and logical evaluations of matters through an in-depth review of a multitude of factors and rationalizations. Decoupled multi-task learning not only overcomes the limitations of single-task paradigms but also meets the requirements for comprehensiveness and objectivity in distinguishing things. Therefore, we propose a self-supervised multi-task learning network with object-level spatiotemporal decoupling in this paper. We decouple the video anomaly detection task in both spatial and temporal dimensions to construct multiple proxy tasks, optimizing overall performance through joint learning. Specifically, we first utilize a pre-trained object detector to construct spatiotemporal context cubes. Then, on the decoupled dimensions, we design several related proxy tasks, adjusting the context cube structure to accommodate each proxy task, and providing reliable information for anomaly detection through joint training. In the end, our method achieved performance that surpasses existing methods on three traffic road datasets, namely UCSD Ped2, Avenue, and ShanghaiTech, with AUCs of 99.6%, 95.6%, and 87.7%, respectively. It demonstrates the potential of generative AI-powered anomaly detection systems in advancing intelligent transportation applications.