Person Re-Identification by Video Ranking
Abstract
Current person re-identification (re-id) methods typically rely on single-frame imagery features, and ignore space-time information from image sequences. Single-frame (single-shot) visual appearance matching is inherently limited for person re-id in public spaces due to visual ambiguity arising from non-overlapping camera views where viewpoint and lighting changes can cause significant appearance variation. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy image sequences of people where more reliable space-time features can be extracted, whilst simultaneously to learn a video ranking function for person re-id. Also, we introduce a new image sequence re-id dataset (iLIDS-VID) based on the i-LIDS MCT benchmark data. Using the iLIDS-VID and PRID 2011 sequence re-id datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-shot/multi-shot based re-id methods.
Cite
Text
Wang et al. "Person Re-Identification by Video Ranking." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10593-2_45Markdown
[Wang et al. "Person Re-Identification by Video Ranking." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/wang2014eccv-person/) doi:10.1007/978-3-319-10593-2_45BibTeX
@inproceedings{wang2014eccv-person,
title = {{Person Re-Identification by Video Ranking}},
author = {Wang, Taiqing and Gong, Shaogang and Zhu, Xiatian and Wang, Shengjin},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {688-703},
doi = {10.1007/978-3-319-10593-2_45},
url = {https://mlanthology.org/eccv/2014/wang2014eccv-person/}
}