Computer and Modernization ›› 2020, Vol. 0 ›› Issue (09): 60-65.doi: 10.3969/j.issn.1006-2475.2020.09.011

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A Feature-enhanced Tri-CNN Pedestrian Re-identification Method


  1. (College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China)
  • Received:2020-02-13 Online:2020-09-24 Published:2020-09-24

Abstract: Aiming at the problem of low resolution of extracted high level features and low recognition rate caused by occlusion in person re-identification, a feature enhanced pedestrian re-identification method based on Tri-CNN is established. Firstly, PCA dimensionality reduction is performed on the image features extracted from the pooling layer, and more discriminating pedestrian features are extracted according to CCA fusion features. Secondly, the spatial recursive model (SRM) is introduced to detect the features of occluded pedestrians in multiple directions, so as to improve the recognition rate of occluded pedestrians. Finally, according to the Euclidean distance measurement criterion, the distance between positive and negative sample pairs is verified respectively, and the loss function of Softmax and Triplet is combined to optimize the network model, so as to determine whether it is the same pedestrian. Experiments on MARS and ETHZ data sets show that the proposed method can effectively solve the problem of general occlusion recognition and significantly improve the accuracy of pedestrian re-identification.

Key words: pedestrian re-identification, Tri-CNN, PCA dimension reduction, canonical correlation analysis (CCA), spatial recurrent model(SRM)

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