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Journal of Graphics ›› 2023, Vol. 44 ›› Issue (1): 33-40.DOI: 10.11996/JG.j.2095-302X.2023010033

• Image Processing and Computer Vision • Previous Articles     Next Articles

Cross modality person re-identification based on residual enhanced attention


  1. College of Computer Science and Technology, China University of Petroleum (East China), Qingdao Shandong 266580, China
  • Online:2023-02-28 Published:2023-02-16
  • Supported by:
    National Key Research and Development Program (2019YFF0301800); National Natural Science Foundation of China (61379106); Shandong Provincial Natural Science Foundation (ZR2013FM036, ZR2015FM011)


Cross modality person re-identification mainly faces two problems: ① Modality discrepancies between infrared and visible images caused by different imaging mechanisms. ② Intra-class discrepancies caused by the insufficient identity discrimination of features. In order to address the above two problems, a cross modality person re-identification method based on residual enhanced attention was proposed to improve the modality invariance and identity discrimination of pedestrian features. First, with non-shared parameters at the shallow network and shared parameters at the deep layer a dual-stream convolutional neural network was designed as the backbone. Then, the problem of global weakening in the existing attention mechanism was analyzed, and a residual enhancement method was designed to solve this problem and improve the performance of the attention mechanism. It was applied to the shallow channel dimension and deep spatial location of the network respectively. Sufficient experiments on the two datasets SYSU-MM01 and RegDB have proved the effectiveness of the method.

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