Vehicle Re-Identification Based on Complementary Features

Cunyuan Gao, Yi Hu, Yi Zhang, Rui Yao, Yong Zhou, Jiaqi Zhao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 590-591


In this work, we present our solution to the vehicle re-identification (vehicle Re-ID) track in AI City Challenge 2020 (AIC2020). The purpose of vehicle Re-ID is to retrieve the same vehicle appeared across multiple cameras, and it could make a great contribution to the Intelligent Traffic System(ITS) and smart city. Due to the vehicle's orientation, lighting and inter-class similarity, it is difficult to achieve robust and discriminative representation feature. For the vehicle Re-ID track in AIC2020, our method is to fuse features extracted from different networks in order to take advantages of these networks and achieve complementary features. For each single model, several methods such as multi-loss, filter grafting, semi-supervised are used to increase the representation ability as better as possible. Top performance in City-Scale Multi-Camera Vehicle Re-Identification demonstrated the advantage of our methods, we got 5-th place in the vehicle Re-ID track of AIC2020.

Related Material

author = {Gao, Cunyuan and Hu, Yi and Zhang, Yi and Yao, Rui and Zhou, Yong and Zhao, Jiaqi},
title = {Vehicle Re-Identification Based on Complementary Features},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}