Ensemble of pose aligned features for person re-identification
WANG Jin
LIU Jie
GAO Chang-xin
SANG Nong
Abstract：Person re-identification is the task of finding a person of interest across a network of cameras. Pose variations and illumination changes are two major challenges for person re-identification. In this paper, we propose a method to tackle these two problems. For pose variations, we train a part dictionary using local appearance-spatial features extracted from densely sampled image patches. Each codeword builds correspondence of a pair of patches to be matched between two images. Images are then aligned by projecting to the common part dictionary. By this method, two images are normalized into a list of corresponding patch pairs. For illumination changes, different color descriptors are extracted from each corresponding patch pair. To obtain better invariance against illumination changes, they are finally combined on the score level by structural output learning. Experiments on the highly challenging viewpoint invariant pedestrian recognition (VIPeR) dataset demonstrate the effectiveness of our approach.
Keywords：person re-identificationpose alignmentstructural SVM
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 837-842 )