Person re-identification (re-id) consists of associating individual across camera network, which is valuable for intelligent video surveillance and has drawn wide attention. Although person re-identification research is making progress, it still faces some challenges such as varying poses, illumination and viewpoints. For feature representation in re-identification, existing works usually use low-level descriptors which do not take full advantage of body structure information, resulting in low representation ability. discrimination. To solve this problem, this paper proposes the mid-level body-structure based feature representation (BSFR) which introduces body structure pyramid for codebook learning and feature pooling in the vertical direction of human body. Besides, varying viewpoints in the horizontal direction of human body usually causes the data missing problem, @math , the appearances obtained in different orientations of the identical person could vary significantly. To address this problem, the orientation driven bag of appearances (ODBoA) is proposed to utilize person orientation information extracted by orientation estimation technic. To properly evaluate the proposed approach, we introduce a new re-identification dataset (Market-1203) based on the Market-1501 dataset and propose a new re-identification dataset (PKU-Reid). Both datasets contain multiple images captured in different body orientations for each person. Experimental results on three public datasets and two proposed datasets demonstrate the superiority of the proposed approach, indicating the effectiveness of body structure and orientation information for improving re-identification performance.
Oliver @math @cite_47 introduce the concept of bag of appearances (BoA) which is a container of color features that fully represents a person by collecting all his different appearances obtained from Kinect. They perform person matching in a probabilistic framework by accumulating the probability of pairwise matching for all of the elements in each bag with appearance and height information. However, BoA contains much redundant data redundancy and ignores the orientation information, resulting in limited accuracy, large storage cost and computation cost. Inspired by the concept of BoA @cite_47 , we introduce ODBoA to store and select the candidate elements in each bag for person matching. Since mid-level feature fusion describes person appearance more comprehensively and is more robust to misalignment and background noise, a mid-level feature pooling strategy is employed to construct a single signature for each person based on BSFR.
@cite_47
2046119740
People re-identification in uncontrolled scenarios is a difficult task since people appearance may significantly vary along time due to changes in illumination, changes in the person pose or the presence of undesired objects in the scene. In order to cope with this temporal variability in the person appearance, we introduce the concept of Bags of Appearances (BoA) to describe each person. A BoA is a container of color features that fully represents a person by collecting all their different appearances along time. Matching of bags is performed in a probabilistic framework by accumulating the probability of matching for all of the elements of each bag. Experiments have been conducted in a real shop where clients were re-identified at the entrance and exit. Results improve state-of-the art methods and confirm that our proposal successfully copes with rough changes in the people appearance.