Pedestrian recognition with a learned metric

Dikmen M., AKBAŞ E., Huang T. S., Ahuja N.

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

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 6495 LNCS
  • Doi Numarası: 10.1007/978-3-642-19282-1_40
  • Basıldığı Şehir: Queenstown
  • Basıldığı Ülke: Yeni Zelanda
  • Sayfa Sayıları: ss.501-512
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

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. © 2011 Springer-Verlag Berlin Heidelberg.