Partial Person Re-Identification
Abstract
We address a new partial person re-identification (re-id) problem, where only a partial observation of a person is available for matching across different non-overlapping camera views. This differs significantly from the conventional person re-id setting where it is assumed that the full body of a person is detected and aligned. To solve this more challenging and realistic re-id problem without the implicit assumption of manual body-parts alignment, we propose a matching framework consisting of 1) a local patch-level matching model based on a novel sparse representation classification formulation with explicit patch ambiguity modelling, and 2) a global part-based matching model providing complementary spatial layout information. Our framework is evaluated on a new partial person re-id dataset as well as two existing datasets modified to include partial person images. The results show that the proposed method outperforms significantly existing re-id methods as well as other partial visual matching methods.
Cite
Text
Zheng et al. "Partial Person Re-Identification." International Conference on Computer Vision, 2015. doi:10.1109/ICCV.2015.531Markdown
[Zheng et al. "Partial Person Re-Identification." International Conference on Computer Vision, 2015.](https://mlanthology.org/iccv/2015/zheng2015iccv-partial/) doi:10.1109/ICCV.2015.531BibTeX
@inproceedings{zheng2015iccv-partial,
title = {{Partial Person Re-Identification}},
author = {Zheng, Wei-Shi and Li, Xiang and Xiang, Tao and Liao, Shengcai and Lai, Jianhuang and Gong, Shaogang},
booktitle = {International Conference on Computer Vision},
year = {2015},
doi = {10.1109/ICCV.2015.531},
url = {https://mlanthology.org/iccv/2015/zheng2015iccv-partial/}
}