An End-to-end Heterogeneous Restraint Network for RGB-D Cross-modal Person Re-identification

Author:

Wu Jingjing1ORCID,Jiang Jianguo2,Qi Meibin2,Chen Cuiqun1,Zhang Jingjing1

Affiliation:

1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei city, Anhui Province, China

2. Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education, School of Computer Science and Information Engineering, Hefei University of Technology, Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei, China

Abstract

The RGB-D cross-modal person re-identification (re-id) task aims to identify the person of interest across the RGB and depth image modes. The tremendous discrepancy between these two modalities makes this task difficult to tackle. Few researchers pay attention to this task, and the deep networks of existing methods still cannot be trained in an end-to-end manner. Therefore, this article proposes an end-to-end module for RGB-D cross-modal person re-id. This network introduces a cross-modal relational branch to narrow the gaps between two heterogeneous images. It models the abundant correlations between any cross-modal sample pairs, which are constrained by heterogeneous interactive learning. The proposed network also exploits a dual-modal local branch, which aims to capture the common spatial contexts in two modalities. This branch adopts shared attentive pooling and mutual contextual graph networks to extract the spatial attention within each local region and the spatial relations between distinct local parts, respectively. Experimental results on two public benchmark datasets, that is, the BIWI and RobotPKU datasets, demonstrate that our method is superior to the state-of-the-art. In addition, we perform thorough experiments to prove the effectiveness of each component in the proposed method.

Funder

National Natural Science Foundation of China

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

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2. A Multi-scale Feature Embedding Extension Network for RGB-D Cross-modal Person Re-identification;2024 7th World Conference on Computing and Communication Technologies (WCCCT);2024-04-12

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