DAIR-V2XReid: A New Real-World Vehicle-Infrastructure Cooperative Re-ID Dataset and Cross-Shot Feature Aggregation Network Perception Method
Hai Wang, Yaqing Niu, Long Chen, Yicheng Li, Miguel Ángel Sotelo, Zhixiong Li, Yingfeng Cai
Abstract
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, we 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, we propose a cross-stage feature fusion module, which integrates low-order semantics with high-order semantics. Finally, we use multi-directional attention network to achieve the final feature extraction. The experimental results show that our proposed CFA-Net method achieves new state-of-the-art in DAIR-V2XReid, with mAP of 58.47%.