Robust Multi-Modality Person Re-identification

To avoid the illumination limitation in visible person reidentification (Re-ID) and the heterogeneous issue in crossmodality Re-ID, we propose to utilize complementary advantages of multiple modalities including visible (RGB), near infrared (NI) and thermal infrared (TI) ones for robust person Re-ID. A novel progressive fusion network is designed to learn effective multi-modal features from single to multiple modalities and from local to global views. Our method works well in diversely challenging scenarios even in the presence of missing modalities. Moreover, we contribute a comprehensive benchmark dataset, RGBNT201, including 201 identities captured from various challenging conditions, to facilitate the research of RGB-NI-TI multi-modality person Re-ID. Comprehensive experiments on RGBNT201 dataset comparing to the state-of-the-art methods demonstrate the contribution of multi-modality person Re-ID and the effectiveness of the proposed approach, which launch a new benchmark and a new baseline for multi-modality person Re-ID.

Authors

• Aihua Zheng School of Artificial Intelligence, Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University
• Zi Wang School of Computer Science and Technology, Anhui University, Hefei, China
• Zihan Chen University of Virginia, Charlottesville
• Chenglong Li Anhui Provincial Key Laboratory of Security Artificial Intelligence, School of Artificial Intelligence, Anhui University, China
• Jin Tang Anhui University

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