Computer Science ›› 2020, Vol. 47 ›› Issue (6): 164-169.doi: 10.11896/jsjkx.190500013

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Person Re -identification Fusing Viewpoint Mechanism and Pose Estimation

PEI Jia-zhen, XU Zeng-chun, HU Ping   

  1. College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China
  • Received:2019-05-05 Online:2020-06-15 Published:2020-06-10
  • About author:PEI Jia-zhen,born in 1995,master.His main research interests include person re-identification,computer vision and deep learning.
    XU Zeng-chun,born in 1973,engineer.Her main research interests include computer science and technology,wireless sensor networks and intelligence computation.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61672279),National Key R&D Program of China (2017YFC0805605) and Key R&D Program of Jiangsu Province (BE2017617)

Abstract: Person re-identification is a very challenging task in video surveillance.Person have significant changes in appearance due to occlusion and differences in illumination,posture and perspective,which will ultimately have a great impact on the accuracy of person re-identification.To overcome these difficulties,this paper proposes a method for person re-identification based on viewpoint mechanism and pose estimation.First,the pose estimation algorithm Openpose is used to locate the joint points of person.Then,view discrimination is performed on the image to obtain viewpoint information.Local regions based on viewpoint information and joint point locations is proposed to generate a partial image.Next,the global image and the partial image are input into the CNN simultaneously to extract features.Finally,in order to obtain a more robust feature representation,the feature fusion network is used to fuse the global and local features.Experimental results show that the proposed method has higher person re-identification accuracy.On CHUK03 dataset,the value of rank1 reaches 71.3%,and on Market1501 dataset and DukeMTMC-reID dataset,the mAP reaches 63.2% and 60.5%,respectively.Therefore,the proposed methokd can well cope with person attitude changes,pose changes and other issues.

Key words: Camera viewpoint, Pose estimation, Deep learning, Person re-identification, Feature fusion

CLC Number: 

  • TP391
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