Research on Pedestrian and Vehicle Detection and Tracking Based on Deep Learning
This paper proposes a multi-objective detection and tracking algorithm combining improved YOLOv5 and improved Deep SORT to address the issues of insufficient detection accuracy,lost tracking targets,and identity switching in pedestrian and vehicle's multi-target detection and tracking.Replacing binary cross entropy loss function with Varifocal Loss in the detection phase,combined with CA attention mechanism and DIoU_NMS algorithm.During the tracking phase,replace the feature extraction network of the REID module of Deep SORT with EfficientNetV2-S.In COCO dataset detection,map@0.5 reaches 78%,an improvement of 4.5%compared to the original model.On the MOT16 dataset tracking,the MOTA reaches 58.1,an improvement of 5.7 compared to the original model.The IDswitch is reduced by 516 times,which is equivalent to a reduction of 55.1%.The test results show that the algorithm has good practical application value.
Deep Learningobject detectionobject trackingcomputer vision
国家科技期刊平台
NSTL
万方数据
维普
