A Vehicle Re-Identification Method Based on Fine-Grained Features and Metric Learning

Abstract

To solve the problem that the global features extracted by the ResNet-50 network has insufficient recognition capability in similar vehicle re-identification task, a new Re-ID method combining metric learning is proposed. Firstly, the fine-grained features of vehicles are extracted by using triplet constraints, and then combined with the global features extracted by the backbone network as vehicle features. Secondly, the similarity of different vehicle features is judged and ranked by Euclidean distance, so as to obtain more accurate results. Finally, a comparative experiment is conducted on the VeRi-776 dataset for different network models. The results show that our method has high recognition accuracy in Re-ID tasks. Compared with ResNet-50, the mean average accuracy (mAP) is improved by 2.30 %, rank-l increased by 2.31 %, and the rank-5 increased by 2.05 %. It is verified that this model can effectively improve the recognition accuracy in vehicle Re-ID.