The Improved Joint Bayesian Method for Person Re-identification Across Different Camera
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초록
Due to the view point, illumination, personal gait and other background situation, person re-identificationacross cameras has been a challenging task in video surveillance area. In order to address the problem, a novelmethod called Joint Bayesian across different cameras for person re-identification (JBR) is proposed. Motivatedby the superior measurement ability of Joint Bayesian, a set of Joint Bayesian matrices is obtained by learningwith different camera pairs. With the global Joint Bayesian matrix, the proposed method combines thecharacteristics of multi-camera shooting and person re-identification. Then this method can improve thecalculation 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, theMarket-1501 and DukeMTMC-reID. The RANK-1 accuracy significantly increases about 3% and 4%, and themaximum a posterior (MAP) improves about 1% and 4%, respectively.
Abstract
Due to the view point, illumination, personal gait and other background situation, person re-identificationacross cameras has been a challenging task in video surveillance area. In order to address the problem, a novelmethod called Joint Bayesian across different cameras for person re-identification (JBR) is proposed. Motivatedby the superior measurement ability of Joint Bayesian, a set of Joint Bayesian matrices is obtained by learningwith different camera pairs. With the global Joint Bayesian matrix, the proposed method combines thecharacteristics of multi-camera shooting and person re-identification. Then this method can improve thecalculation 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, theMarket-1501 and DukeMTMC-reID. The RANK-1 accuracy significantly increases about 3% and 4%, and themaximum a posterior (MAP) improves about 1% and 4%, respectively.
참고문헌 (21)
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