DAIR-V2XReid: A New Real-World Vehicle-Infrastructure Cooperative Re-ID Dataset and Cross-Shot Feature Aggregation Network Perception Method

As an emerging research field, vehicle re-identification (Re-ID) can realize identity search between the vehicles, which plays an important role in the over-the-horizon perception of Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD). At present, due to the lack of data sets, the relevant research on Vehicle-Infrastructure Cooperative (VIC) Re-ID can only be evaluated in the cross-view monitoring test set which leads to the lack of persuasion of the research. Therefore, based on the DAID-V2X dataset of Tsinghua University, this paper constructs a VIC Re-ID dataset “DAIR-V2XReid” from real vehicle scenarios through vehicle-road end target tag association, thereby making it better applicable to the research of VIC Re-ID. Owing to different task scenarios, existing algorithms trained on monitoring test sets are unable to effectively complete the Re-ID task in this new dataset. Therefore, Cross-shot Feature Aggregation Network (CFA-Net) is also proposed in this paper, to tackle the case where a vehicle becomes unrecognizable due to a large change in its visual appearance across different cameras. Firstly, the authors put forward a camera embedding module and add it to the Backbone, to group different cameras and solve the problem of cross-shot perspective mutation. Secondly, in order to address the situation where background and vehicle division are not distinguishable, the authors propose a cross-stage feature fusion module, which integrates low-order semantics with high-order semantics. Finally, the authors use multi-directional attention network to achieve the final feature extraction. The experimental results show that the authors' proposed CFA-Net method achieves new state-of-the-art in DAIR-V2XReid, with mAP of 58.47%.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2024.3367723
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Wang, Hai
  • 0000-0002-9136-8091
  • Niu, Yaqing
  • Chen, Long
  • 0000-0002-2079-3867
  • Li, Yicheng
  • 0000-0003-1492-3116
  • Sotelo, Miguel Angel
  • 0000-0001-8809-2103
  • Li, Zhixiong
  • 0000-0003-4067-0669
  • Cai, Yingfeng
  • 0000-0002-0633-9887
• Publication Date: 2024-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 9058-9068
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 8
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Automatic vehicle identification; Cameras; Data files; Data fusion; Vehicle to infrastructure communications
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01936681
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 12 2024 9:43AM