Heterogeneous Relational Complement for Vehicle Re-identification

TL;DR

This paper introduces a novel vehicle re-identification method using a heterogeneous relational complement network and a new evaluation measure, significantly improving cross-view recognition accuracy and evaluation robustness.

Contribution

The paper presents a new Heterogeneous Relational Complement Network and a cross-camera generalization measure, advancing vehicle re-identification and evaluation methods.

Findings

Achieves state-of-the-art results on VeRi-776, VehicleID, and VERI-Wild datasets.

Introduces a graph-based relation module for feature embedding.

Proposes a new evaluation measure with position-sensitivity and cross-camera penalties.

Abstract

The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations. In this paper, we propose to solve this problem from two aspects: constructing robust feature representations and proposing camera-sensitive evaluations. We first propose a novel Heterogeneous Relational Complement Network (HRCN) by incorporating region-specific features and cross-level features as complements for the original high-level output. Considering the distributional differences and semantic misalignment, we propose graph-based relation modules to embed these heterogeneous features into one unified high-dimensional space. On the other hand, considering the deficiencies of cross-camera evaluations in existing measures (i.e., CMC and AP), we then propose a Cross-camera Generalization Measure (CGM) to improve the evaluations by introducing position-sensitivity and cross-camera generalization penalties. We further construct a new benchmark of existing models with our proposed CGM and experimental results reveal that our proposed HRCN model achieves new state-of-the-art in VeRi-776, VehicleID, and VERI-Wild.