Vehicle Tracking Algorithm Based on Transformer's Improved YOLOv5+DeepSORT
In order to solve the shortcomings of traditional object detection and tracking algorithms,such as low detection accuracy,poor global perception ability,poor recognition ability of occlusion and small target objects,this paper proposed a vehicle tracking method based on YOLOv5 and DeepSORT algorithm improved by lightweight Transformer.Firstly,the EfficientFormerV2 model was used to improve the YOLOv5 algorithm model to enhance the target detection ability of the vehicle,and then the advantages of the Swin model were used to improve the Re-Identification module in the DeepSORT multi-target tracking algorithm to enhance the tracking ability and accuracy of the vehicle.Finally,the dataset KITTI and VeRi were used to carry out comparative experiments and ablation experiments.The results show that under complex conditions,the performance of the proposed method is significantly improved in vehicle occlusion and small target recognition,with an average accuracy of 96.7％,an increase of 9.547％in target tracking,and a reduction of 26.4％in the total number of ID switching.
万方数据
维普
