Skip to main content
placeholder image

Reducing Background Induced Domain Shift for Adaptive Person Re-Identification

Journal Article


Abstract


  • Cross-domain person re-identification (Re-ID) is a challenging and important task in monitoring safety and procedure compliance of industrial work places. In this paper, a novel method is proposed to reduce background induced domain shift for adaptive person Re-ID. Specifically, a foreground-background joint clustering module is proposed to extract discriminative foreground and background features and an attention-based feature disentanglement module is designed to reduce the interference of background with the extraction of discriminative foreground features. Experimental results on three widely used person Re-ID benchmarking datasets (Market-1501, DukeMTMC-reID, and MSMT17) have demonstrated that the proposed method achieves promising performance compared with the state-of-the-art methods.

Publication Date


  • 2022

Citation


  • Lei, J., Qin, T., Peng, B., Li, W., Pan, Z., Shen, H., & Kwong, S. (2022). Reducing Background Induced Domain Shift for Adaptive Person Re-Identification. IEEE Transactions on Industrial Informatics. doi:10.1109/TII.2022.3210589

Scopus Eid


  • 2-s2.0-85139478578

Web Of Science Accession Number


Volume


Issue


Place Of Publication


Abstract


  • Cross-domain person re-identification (Re-ID) is a challenging and important task in monitoring safety and procedure compliance of industrial work places. In this paper, a novel method is proposed to reduce background induced domain shift for adaptive person Re-ID. Specifically, a foreground-background joint clustering module is proposed to extract discriminative foreground and background features and an attention-based feature disentanglement module is designed to reduce the interference of background with the extraction of discriminative foreground features. Experimental results on three widely used person Re-ID benchmarking datasets (Market-1501, DukeMTMC-reID, and MSMT17) have demonstrated that the proposed method achieves promising performance compared with the state-of-the-art methods.

Publication Date


  • 2022

Citation


  • Lei, J., Qin, T., Peng, B., Li, W., Pan, Z., Shen, H., & Kwong, S. (2022). Reducing Background Induced Domain Shift for Adaptive Person Re-Identification. IEEE Transactions on Industrial Informatics. doi:10.1109/TII.2022.3210589

Scopus Eid


  • 2-s2.0-85139478578

Web Of Science Accession Number


Volume


Issue


Place Of Publication