Learning Implicit Transfer for Person Re-Identification
Abstract
This paper proposes a novel approach for pedestrian re-identification. Previous re-identification methods use one of 3 approaches: invariant features; designing metrics that aim to bring instances of shared identities close to one another and instances of different identities far from one another; or learning a transformation from the appearance in one domain to the other. Our implicit approach models camera transfer by a binary relation R = ( x , y )| x and y describe the same person seen from cameras A and B respectively. This solution implies that the camera transfer function is a multi-valued mapping and not a single-valued transformation, and does not assume the existence of a metric with desirable properties. We present an algorithm that follows this approach and achieves new state-of-the-art performance.
Cite
Text
Avraham et al. "Learning Implicit Transfer for Person Re-Identification." European Conference on Computer Vision Workshops, 2012. doi:10.1007/978-3-642-33863-2_38Markdown
[Avraham et al. "Learning Implicit Transfer for Person Re-Identification." European Conference on Computer Vision Workshops, 2012.](https://mlanthology.org/eccvw/2012/avraham2012eccvw-learning/) doi:10.1007/978-3-642-33863-2_38BibTeX
@inproceedings{avraham2012eccvw-learning,
title = {{Learning Implicit Transfer for Person Re-Identification}},
author = {Avraham, Tamar and Gurvich, Ilya and Lindenbaum, Michael and Markovitch, Shaul},
booktitle = {European Conference on Computer Vision Workshops},
year = {2012},
pages = {381-390},
doi = {10.1007/978-3-642-33863-2_38},
url = {https://mlanthology.org/eccvw/2012/avraham2012eccvw-learning/}
}