Pedestrian Recognition with a Learned Metric


DIKMEN M., Akbas E. , HUANG T. S. , Ahuja N.

10th Asian Conference on Computer Vision, Queenstown, Guyana, 8 - 12 Kasım 2010, cilt.6495, ss.501-512 identifier

  • Cilt numarası: 6495
  • Basıldığı Şehir: Queenstown
  • Basıldığı Ülke: Guyana
  • Sayfa Sayısı: ss.501-512

Özet

This paper presents a new method for viewpoint invariant pedestrian recognition problem. We use a metric learning framework to obtain a robust metric for large margin nearest neighbor classification with rejection (i.e., classifier will return no matches if all neighbors are beyond a certain distance). The rejection condition necessitates the use of a uniform threshold for a. maximum allowed distance for deeming a, pair of images a match. In order to handle the rejection case, we propose a novel cost similar to the Large Margin Nearest Neighbor (LMNN) method and call our approach Large Margin Nearest Neighbor with Rejection (LMNN-R). Our method is able to achieve significant improvement over previously reported results on the standard Viewpoint Invariant Pedestrian Recognition (VIPeR [1]) dataset.