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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Pedestrian Recognition with a Learned Metric
verfasst von : Mert Dikmen, Emre Akbas, Thomas S. Huang, Narendra Ahuja
Erschienen in: Computer Vision – ACCV 2010
Verlag: Springer Berlin Heidelberg
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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.