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Existing unsupervised person re-identification methods only rely on visualclues to match pedestrians under different cameras. Since visual data isessentially susceptible to occlusion, blur, clothing changes, etc., a promisingsolution is to introduce heterogeneous data to make up for the defect of visualdata. Some works based on full-scene labeling introduce wireless positioning toassist cross-domain person re-identification, but their GPS labeling of entiremonitoring scenes is laborious. To this end, we propose to explore unsupervisedperson re-identification with both visual data and wireless positioningtrajectories under weak scene labeling, in which we only need to know thelocations of the cameras. Specifically, we propose a novel unsupervisedmultimodal training framework (UMTF), which models the complementarity ofvisual data and wireless information. Our UMTF contains a multimodal dataassociation strategy (MMDA) and a multimodal graph neural network (MMGN). MMDAexplores potential data associations in unlabeled multimodal data, while MMGNpropagates multimodal messages in the video graph based on the adjacency matrixlearned from histogram statistics of wireless data. Thanks to the robustness ofthe 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. Extensiveexperimental results conducted on two challenging datasets, i.e., WP-ReID andDukeMTMC-VideoReID demonstrate the effectiveness of the proposed method.

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