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Pedestrian Recognition with a Learned Metric.
- Source :
- Computer Vision - Accv 2010; 2011, p501-512, 12p
- Publication Year :
- 2011
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783642192814
- Database :
- Complementary Index
- Journal :
- Computer Vision - Accv 2010
- Publication Type :
- Book
- Accession number :
- 76856347
- Full Text :
- https://doi.org/10.1007/978-3-642-19282-1_40