JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
The Improved Joint Bayesian Method for Person Re-identification Across Different Camera |
| 영문제목(English Title) |
The Improved Joint Bayesian Method for Person Re-identification Across Different Camera |
| 저자(Author) |
Ligang Hou
Yingqiang Guo
Jiangtao Cao
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| 원문수록처(Citation) |
VOL 15 NO. 04 PP. 0785 ~ 0796 (2019. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English 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.
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| 키워드(Keyword) |
Joint Bayesian
Multi-Camera Shooting
Person Re-identification
Superior Measurement
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| 파일첨부 |
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