A Unsupervised Person Re-identification Method Using Model Based Representation and Ranking

Chao Liang, Bingyue Huang, Ruimin Hu, Chunjie Zhang, Xiao-Yuan Jing, Jing Xiao

Abstract: As a core technique supporting the multi-camera tracking task, person re-identification attracts increasing research interests in both academic and industrial communities. Its aim is to match individuals across a group of spatially non-overlapping surveillance cameras, which are usually interfered by various imaging conditions and object motions. Current methods mainly focus on robust feature representation and accurate distance measure, where intensive computations and expensive training samples prohibit their practical applications. To address the above problems, this paper proposes a new unsupervised person re-identification method featured by its competitive accuracy and high efficiency. Both merits stem from model based person image representation and ranking, with which, merely 4-dimension pixel-level features can achieve over 20% matching rate at Rank 1 on the challenging VIPeR dataset.