Person Re-identification using Deformable Patch Metric Learning
In this paper, we propose to learn appearance measures for patches that are combined using a spring model for addressing the correspondence problem.
March 7, 2016
IEEE Winter Conference on Applications of Computer Vision (WACV) 2016
Authors
Slawomir Bak (Disney Research)
Peter Carr (Disney Research)
Person Re-identification using Deformable Patch Metric Learning
The methodology for finding the same individual in a network of cameras must deal with significant changes in appearance caused by variations in illumination, viewing angle and a person’s pose. Re-identification requires solving two fundamental problems: (1) determining a distance measure between features extracted from different cameras that copes with illumination changes (metric learning); and (2) ensuring that matched features refer to the same body part (correspondence). Most metric learning approaches focus on finding a robust distance measure between bounding box images, neglecting the alignment aspects. In this paper, we propose to learn appearance measures for patches that are combined using a spring model for addressing the correspondence problem. We validated our approach on the VIPeR, i-LIDS and CUHK01 datasets achieving new state of the art performance.
