1 research outputs found
Orientation Driven Bag of Appearances for Person Re-identification
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, , 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.Comment: 13 pages, 15 figures, 3 tables, submitted to IEEE Transactions on
Circuits and Systems for Video Technolog