Vehicle Re-Identification Based on Complementary Features

TL;DR

This paper presents a vehicle re-identification method that fuses features from multiple networks and employs various training techniques, achieving top performance in the AI City Challenge 2020.

Contribution

It introduces a complementary feature fusion approach combined with multi-loss, filter grafting, and semi-supervised methods for robust vehicle re-identification.

Findings

Achieved 5th place in AIC2020 vehicle Re-ID track

Demonstrated the effectiveness of feature fusion and training strategies

Outperformed several existing methods in city-scale vehicle Re-ID

Abstract

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, and we got 5-th place in the vehicle Re-ID track of AIC2020. The codes are available at https://github.com/gggcy/AIC2020_ReID.