Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene Labeling

TL;DR

This paper introduces an unsupervised multimodal framework combining visual data and wireless positioning for person re-identification, reducing reliance on labor-intensive scene labeling and improving robustness to visual noise.

Contribution

It proposes a novel unsupervised multimodal training framework (UMTF) with a data association strategy and graph neural network, leveraging weak scene labels for cross-domain person re-identification.

Findings

Effective on WP-ReID and DukeMTMC-VideoReID datasets.

Outperforms existing methods in accuracy and robustness.

Capable of learning without human-labeled data.

Abstract

Existing unsupervised person re-identification methods only rely on visual clues to match pedestrians under different cameras. Since visual data is essentially susceptible to occlusion, blur, clothing changes, etc., a promising solution is to introduce heterogeneous data to make up for the defect of visual data. Some works based on full-scene labeling introduce wireless positioning to assist cross-domain person re-identification, but their GPS labeling of entire monitoring scenes is laborious. To this end, we propose to explore unsupervised person re-identification with both visual data and wireless positioning trajectories under weak scene labeling, in which we only need to know the locations of the cameras. Specifically, we propose a novel unsupervised multimodal training framework (UMTF), which models the complementarity of visual data and wireless information. Our UMTF contains a multimodal data association strategy (MMDA) and a multimodal graph neural network (MMGN). MMDA explores potential data associations in unlabeled multimodal data, while MMGN propagates multimodal messages in the video graph based on the adjacency matrix learned from histogram statistics of wireless data. Thanks to the robustness of the wireless data to visual noise and the collaboration of various modules, UMTF is capable of learning a model free of the human label on data. Extensive experimental results conducted on two challenging datasets, i.e., WP-ReID and DukeMTMC-VideoReID demonstrate the effectiveness of the proposed method.