Computer Science ›› 2020, Vol. 47 ›› Issue (10): 180-186.doi: 10.11896/jsjkx.191100061

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Cross-modality Person Re-identification Framework Based on Improved Hard Triplet Loss

LI Hao, TANG Min, LIN Jian-wu, ZHAO Yun-bo   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2019-11-08 Revised:2020-04-03 Online:2020-10-15 Published:2020-10-16
  • About author:LI Hao,born in 1995,postgraduate.His main research interests include deep learning and person reidentification.
    ZHAO Yun-bo,born in 1981,Ph.D,professor.His main research interests include networked control systems,AI enabled automation,human-machine integrated intelligence,and systems bio-logy.
  • Supported by:
    National Natural Science Foundation of China (61673350)

Abstract: In order to improve the recognition accuracy of cross-modality person re-identification,a feature learning framework based on improved hard triplet loss is proposed.Firstly,traditional hard triplet loss is converted to a global one.Secondly,intra-modality and cross-modality triplet losses are designed to match the global one for model training based on the intra-modality and cross-modality variations.On the basis of improving the hard triplet loss,for the first time the attribute features are designed to increase the ability of the model to extract features in the cross-modality person re-identification model.Finally,for the category imbalance problem,Focal Loss is used to replace the traditional Cross Entropy loss for model training.Compared with existing algorithms,the proposed approach behaves the best on the publicly available RegDB dataset,with an increase of 1.9%~6.4% in all evaluation indicators.In addition,ablation experiments also show that all the three methods can improve the feature ability extraction of the model.

Key words: Attribution feature, Category imbalance, Cross-modality, Hard triplet loss, Person re-identification

CLC Number: 

  • TP391.41
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