Intra-Camera Supervised Person Re-Identification

doi:10.1007/s11263-021-01440-4

Intra-Camera Supervised Person Re-Identification

2021-02-26

27

OA

AI

DOI：10.1007/s11263-021-01440-4

Original

Save

View PDF

Abstract

En 中文

Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation process, leading to poor scalability in practical re-id applications. On the other hand unsupervised re-id methods do not need identity label information, but they usually suffer from much inferior and insufficient model performance. To overcome these fundamental limitations, we propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process, significantly reducing the amount of human annotation efforts. Consequently, it gives rise to a more scalable and more feasible setting, which we call Intra-Camera Supervised (ICS) person re-id, for which we formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method. Specifically, MATE is designed for self-discovering the cross-camera identity correspondence in a per-camera multi-task inference framework. Extensive experiments demonstrate the cost-effectiveness superiority of our method over the alternative approaches on three large person re-id datasets. For example, MATE yields 88.7% rank-1 score on Market-1501 in the proposed ICS person re-id setting, significantly outperforming unsupervised learning models and closely approaching conventional fully supervised learning competitors.

Keywords:

Person re-identification

Intra-camera labelling

Cross-camera labelling

Multi-task learning

Multi-label learning

Intra-Camera Supervised Person Re-Identification

Abstract

AI Summary

Journal

Organization