Partial Person Re-Identification
Wei-Shi Zheng, Xiang Li, Tao Xiang, Shengcai Liao, Jianhuang Lai, Shaogang Gong
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.
BibTeX
@inproceedings{iccv2015_partialpersonrei,
title = {Partial Person Re-Identification},
author = {Wei-Shi Zheng and Xiang Li and Tao Xiang and Shengcai Liao and Jianhuang Lai and Shaogang Gong},
booktitle = {ICCV 2015},
year = {2015}
}