Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (2): 49-57.doi: 10.16088/j.issn.1001-6600.2021081303

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Road Vehicle Tracking Algorithm Based on Improved YOLOv5

ZHANG Wenlong, NAN Xinyuan*   

  1. School of Electrical Engineering, Xinjiang University, Urumchi Xinjiang 830047, China
  • Received:2021-08-13 Revised:2021-09-29 Published:2022-05-31

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Abstract: To solve the problem that it's difficult for the large amount of network parameters and calculations for existing multi-object tracking algorithm to meet the real-time requirements of mobile devices, a road vehicle multi-object tracking algorithm is proposed by improving the JDE tracking algorithm. Firstly, in order to improve the tracking accuracy of the algorithm and reduce the number of ID switching, the association fusion network is used to solve the competition problem of multi-task learning in the JDE algorithm. Secondly, in order to reduce the complexity of the model and improve the real-time detection speed of the model, the improved EfficientNetV2 is used to rebuild the feature extraction network in YOLOv5. Finally, the improved YOLOv5 detection algorithm is combined with the JDE tracking algorithm to achieve multi-object tracking of road vehicles. The experimental results show that compared with the original JDE tracking algorithm, the proposed method improves MOTA by 0.3percentage point and tracking speed by about 43.2%. It can meet the speed requirements for vehicle tracking in actual autonomous driving scenarios.

Key words: vehicle tracking, EfficientNet, channel attention, associative fusion network, YOLOv5

CLC Number: 

  • TP391.41
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