Home Journals Articles Updates About
CN
image
收藏切换
Vehicle Tracking Algorithm Based on Transformer’s Improved YOLOv5+DeepSORT
收藏切换
PDF
Shuilong He1, 2, Jingjia Zhang1, Linjun Zhang1, Deyun Mo2
Automobile Technology | 2024, (7) : 9 - 16
Less
收藏切换
Automobile Technology | 2024, (7): 9-16
• Feature Topic on Motion Planning and Control Techniques •
Vehicle Tracking Algorithm Based on Transformer’s Improved YOLOv5+DeepSORT
Full
Shuilong He1, 2, Jingjia Zhang1, Linjun Zhang1, Deyun Mo2
Affiliations
  • 1 Guilin University of Electronic Technology, Guilin 541004
  • 2 Guilin University of Aerospace Technology,Guilin 541004
Published: 2024-07-24 doi: 10.19620/j.cnki.1000-3703.20231097
Outline
收藏切换

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.

YOLOv5  /  Vehicle detection  /  DeepSORT  /  Transformer
Shuilong He, Jingjia Zhang, Linjun Zhang, Deyun Mo. Vehicle Tracking Algorithm Based on Transformer’s Improved YOLOv5+DeepSORT[J]. Automobile Technology, 2024 , (7) : 9 -16 . DOI: 10.19620/j.cnki.1000-3703.20231097
Appendix
Less
Year 2024 volume Issue 7
PDF
231
83
Cite this Article
BibTeX
Article Info
doi: 10.19620/j.cnki.1000-3703.20231097
  • Online Date：2025-12-22
  • Published：2024-07-24
Article Data
Affiliations
History
Funding
Affiliations
    1 Guilin University of Electronic Technology, Guilin 541004
    2 Guilin University of Aerospace Technology,Guilin 541004
References
Share
https://castjournals.cast.org.cn/joweb/qcjs/EN/10.19620/j.cnki.1000-3703.20231097
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House