Cross-View Asymmetric Metric Learning for Unsupervised Person Re-Identification

Abstract

While metric learning is important for Person re-identification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount of data over multiple disjoint camera views is available but not labelled. To overcome the problem, we propose an unsupervised asymmetric metric learning model for unsupervised RE-ID. Our model aims to learn an asymmetric metric, i.e., specific projection for each view, effectively based on clustering on cross-view person images. Our model finds a shared space where view-specific bias is alleviated and thus better matching performance can be achieved. Extensive experiments have been conducted on a baseline and five large-scale RE-ID datasets to demonstrate the effectiveness of the proposed model. Through the comparison, we show that our unsupervised asymmetric metric model works much more suitable for unsupervised RE-ID as compared to classical unsupervised metric learning models. We also compare existing unsupervised RE-ID methods, and our model outperforms them with notable margins, and especially we report the performance on large-scale unlabelled RE-ID dataset, which is unfortunately less concerned in literatures.