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The Improved Joint Bayesian Method for Person Re-identification Across Different Camera

  • Hou, Ligang (School of Information and Control Engineering, Liaoning Shihua University) ;
  • Guo, Yingqiang (School of Information and Control Engineering, Liaoning Shihua University) ;
  • Cao, Jiangtao (School of Information and Control Engineering, Liaoning Shihua University)
  • Received : 2017.11.15
  • Accepted : 2018.05.01
  • Published : 2019.08.31

Abstract

Due to the view point, illumination, personal gait and other background situation, person re-identification across cameras has been a challenging task in video surveillance area. In order to address the problem, a novel method called Joint Bayesian across different cameras for person re-identification (JBR) is proposed. Motivated by the superior measurement ability of Joint Bayesian, a set of Joint Bayesian matrices is obtained by learning with different camera pairs. With the global Joint Bayesian matrix, the proposed method combines the characteristics of multi-camera shooting and person re-identification. Then this method can improve the calculation precision of the similarity between two individuals by learning the transition between two cameras. For investigating the proposed method, it is implemented on two compare large-scale re-ID datasets, the Market-1501 and DukeMTMC-reID. The RANK-1 accuracy significantly increases about 3% and 4%, and the maximum a posterior (MAP) improves about 1% and 4%, respectively.

Keywords

Joint Bayesian;Multi-Camera Shooting;Person Re-identification;Superior Measurement

Acknowledgement

Supported by : Natural Science Foundation of Liaoning Province

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