Pedestrian recognition with a learned metric

Date

2011-03-16

Author

Dikmen, Mert
Akbaş, Emre
Huang, Thomas S.
Ahuja, Narendra

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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. © 2011 Springer-Verlag Berlin Heidelberg.

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79952493654&origin=inward
https://hdl.handle.net/11511/106865

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Department of Computer Engineering, Conference / Seminar