基于时空交互信息融合的车辆违规超车识别OA

To improve the accuracy of vehicle illegal overtaking recognition,this paper proposes spatiotemporal interaction information fusion for vehicle illegal overtaking recognition algorithm.The algorithm is built upon the TimeSformer architecture as the backbone model.Four types of modality information,namely RGB images,optical flow,depth maps,and overtaking interaction graphs,are integrated to construct a unified overtaking information graph.From multiple perspectives,including appearance features,motion information,3D spatial structure,and inter-vehicle interaction relationships,the method performs joint modeling of overtaking behaviors.By introducing divided space-time attention mechanism and multi-modal feature fusion strategy,the proposed approach effectively captures the dynamic evolution of the target vehicle during the overtaking process as well as its spatiotemporal interactions with surrounding vehicles,thereby alleviating the insufficient representation of multi-vehicle interactions in complex traffic scenarios.Experimental results on the PREVENTION dataset show that the proposed algorithm achieves a recognition accuracy of 94.04%for illegal overtaking behaviors,outperforming several existing mainstream algorithms and validating the effectiveness of multimodal spatiotemporal interaction information fusion for complex traffic behavior recognition.