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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Dikmen, M., Akbas, E., Huang, T.S., Ahuja, N. (2011). Pedestrian Recognition with a Learned Metric. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_40
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DOI: https://doi.org/10.1007/978-3-642-19282-1_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19281-4
Online ISBN: 978-3-642-19282-1
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