Pedestrian Recognition with a Learned Metric

Date

2010-11-12

Author

DIKMEN, Mert
Akbaş, Emre
HUANG, Thomas S.
Ahuja, Narendra

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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.

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https://hdl.handle.net/11511/55502

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. DIKMEN, E. Akbaş, T. S. HUANG, and N. Ahuja, “Pedestrian Recognition with a Learned Metric,” 2010, vol. 6495, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55502.