Pedestrian recognition with a learned metric

Authors:
Mert Dikmen;Emre Akbas;Thomas S. Huang;Narendra Ahuja
Affiliations:
Beckman Institute, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign;Beckman Institute, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign;Beckman Institute, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign;Beckman Institute, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Year:
2010

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Abstract

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.