Open-world Person Re-Identification by Multi-Label Assignment Inference
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.98
Abstract
Person re-identification methods have recently made tremendous progress on maximizing re-identification accuracy between camera pairs. However, this line of work mostly shares an critical limitation - it assumes re-identification in a `closed world'. That is, between a known set of people who all appear in both views of a single pair of cameras. This is clearly far from a realistic application scenario. In this study, we take a significant step toward a more realistic `open world' scenario. We consider associating persons observed in more than two cameras where: multiple within-camera detections are possible; different people can transit between different cameras -- so that there is only partial and unknown overlap of identity between people observed by each camera; and the total number of unique people among all cameras is itself unknown. To address this significantly more challenging open world scenario, we propose a novel framework based on online Conditional Random Field (CRF) inference. Experiments demonstrate the robustness of our approach in contrast to the limitations of conventional approaches in the open world context.
Session
Poster Session
Files
Extended Abstract (PDF, 1 page, 144K)Paper (PDF, 11 pages, 254K)
Bibtex File
Citation
Brais Cancela, Tim Hospedales, and Shaogang Gong. Open-world Person Re-Identification by Multi-Label Assignment Inference. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.98
title = {Open-world Person Re-Identification by Multi-Label Assignment Inference},
author = {Cancela, Brais and Hospedales, Tim and Gong, Shaogang},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.98 }
}