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.
The documents contained in these directories are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.