Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (12): 115-125.DOI: 10.3778/j.issn.1002-8331.2102-0033

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Collaborative Learning Method for Cross Modality Person Re-identification

CHEN Kunfeng, PAN Zhisong, WANG Jiabao, SHI Lei, ZHANG Jin, JIAO Shanshan   

  1. College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
  • Online:2021-06-15 Published:2021-06-10



  1. 陆军工程大学 指挥控制工程学院,南京 210007


Cross modality person re-identification is a key technology to realize 24-hour intelligent video surveillance system. This technology is designed to match the visible light image and infrared image of a person with a specific identity in a non-overlapping camera scene, so it faces huge intra-class changes and modality discrepancy. Existing methods are difficult to solve these two major difficulties, which is largely due to the lack of effective mining of feature discrimination and full utilization of multi-source heterogeneous information. In view of the above shortcomings, this paper uses collaborative learning method to design a refined multi-source feature collaborative network, which extracts multiple complementary features for information fusion to enhance the learning ability of the network. Multi-scale and multi-level features are extracted from the backbone convolutional network to realize the collaborative learning of refined features to enhance the discrimination ability of features to deal with intra-class changes. In addition, a modality shared and specific feature collaborative learning module and a cross-modal human semantic self-supervised module are designed to achieve the purpose of multi-source feature collaborative learning, to improve the utilization of multi-source heterogeneous image information, and to resolve modality discrepancy. The effectiveness and advancement of this method have been verified on the SYSU-MM01 and RegDB datasets.

Key words: person re-identification, cross modality, collaborative learning, refined features, multi-source features, information fusion



关键词: 行人再识别, 跨模态, 协同学习, 精细化特征, 多源特征, 信息融合