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Cross-domain unsupervised pedestrian re-identification based on multi-view decomposition

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Abstract

Great improvement has been made in pedestrian re-identification, but the test results in unknown domain are not satisfactory. This is because the pedestrian Re-ID model does not learn good common features between different domains. We found that view-angle related features are invariant in different domains. Based on this, we develop a cross-domain Re-ID method. Specifically, any pedestrian image is decomposed under the constraints of multiple view angles and the pedestrian multi-view features are generated. We propose an improved lightweight capsule network as the multi-view decomposition. The experimental results showed that our method can effectively improve the cross-domain performance of Re-ID.

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Notes

  1. http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html

  2. https://www.kaggle.com/pengcw1/market-1501/data

  3. https://vision.soe.ucsc.edu/node/178/

  4. https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID450S/

  5. https://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-reID.zip

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Acknowledgements

We would like to thank Edmund F. and Rhoda E. Perozzi for extensive professional language and content editing. This work is partially supported by research grants from the National Natural Science Foundation of China(61873178, 61976150), Natural Science Foundation of Shanxi Province (201901D111091), Key Research And Development Projects of Shanxi Province(201803D31038), Key Research And Development Projects of Jinzhong City(Y192006), CERNET Next Generation Internet Technology Innovation Project(NGII20181206), Domestic and Foreign Crop Yield Meteorology Forecast Special Project(RH19100004).

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Authors and Affiliations

  1. College of Information and Computer, Taiyuan University of Technology, 030600, Yuci, China

    Xiaofeng Yang, Qianshan Wang & Haifang Li

  2. College of Computer Engineering, Shanxi Vocational University of Engineering Science and Technology, 030600, Yuci, China

    Xiaofeng Yang

  3. College of Mathematics, Taiyuan University of Technology, 030600, Yuci, China

    Zihao Zhou

  4. Shanxi Climatic Center, 03006, Taiyuan, China

    Zhiwei Wang

  5. College of Computer Science and Technology, Zhejiang University, 310007, Hangzhou, China

    Xi Li

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  1. Xiaofeng Yang
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Correspondence to Haifang Li.

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Yang, X., Zhou, Z., Wang, Q. et al. Cross-domain unsupervised pedestrian re-identification based on multi-view decomposition. Multimed Tools Appl 81, 39387–39408 (2022). https://doi.org/10.1007/s11042-021-11797-w

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