Person Re-Identification by Localizing Discriminative Regions
Tanzila Rahman, Mrigank Rochan and Yang Wang
Abstract
Person re-identification is a challenging task of matching a person’s image across
multiple images captured from different camera views. Recently, deep learning based
approaches have been proposed that show promising performance on this task. However,
most of these approaches use whole image features to compute the similarity between
images. This is not very intuitive since not all the regions in an image contain information about the person identity.
In this paper, we introduce an end-to-end Siamese
convolutional neural network that firstly localizes discriminative salient image regions
and then computes the similarity based on these image regions in conjunction with the
whole image. We use Spatial Transformer Networks (STN) for localizing salient regions.
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DOI
10.5244/C.31.55
https://dx.doi.org/10.5244/C.31.55
Citation
Tanzila Rahman, Mrigank Rochan and Yang Wang. Person Re-Identification by Localizing Discriminative Regions. In T.K. Kim, S. Zafeiriou, G. Brostow and K. Mikolajczyk, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 55.1-55.12. BMVA Press, September 2017.
Bibtex
@inproceedings{BMVC2017_55,
title={Person Re-Identification by Localizing Discriminative Regions},
author={Tanzila Rahman, Mrigank Rochan and Yang Wang},
year={2017},
month={September},
pages={55.1-55.12},
articleno={55},
numpages={12},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
doi={10.5244/C.31.55},
isbn={1-901725-60-X},
url={https://dx.doi.org/10.5244/C.31.55}
}