Global and Local Deep Feature Representation Fusion for Vehicle Re-Identification

Xing Zhao Lee, Hao Wang, Jiangtao Kong, Chi Su, Junliang Xing, Shengmei Shen

Abstract: This paper introduces our submission to the Grand Challenges on Vehicle Re-Identification (ReID) held in the VCIP 2019. Vehicle Re-Identification, which aims to retrieve images of a query vehicle from a large-scale vehicle database, is of great significance to the urban security and city management. Although significant progress has been made in the last decade, vehicle ReID in the wild remains a very challenging problem due to the large intra-class variations of one vehicle instance from viewpoint, illumination, occlusion patterns, and the possibly small inter-class differentiation of different vehicle instances (e.g., two different vehicles of the same model and color). To deal with these challenges, we in this work present a vehicle ReID framework that integrates both the global visual cues along with the local part-based cues to learn discriminative feature representations. In addition, the proposed framework also makes use of the extra information such as brands, models and colors to further improve the performance. Experimental results are performed on the VCIP 2019 VehicleReID dataset and the proposed framework achieved the second place in the competition.