Pedestrian Recognition with a Learned Metric

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.

• Type: Journal
• Year: 2010
• Where: ACCV
• Authors: Mert Dikmen, Emre Akbas, Thomas S. Huang, Narendra Ahuja
• Keywords: ACCV 2010, Computer Vision, Invariant Pedestrian Recognition, Large Margin, Margin Nearest Neighbor