From Indoor To Outdoor: Unsupervised Domain Adaptive Gait Recognition

TL;DR

This paper introduces an unsupervised domain adaptive gait recognition method that transfers knowledge from indoor to outdoor scenes, addressing the challenge of domain shift in gait recognition tasks.

Contribution

It proposes a novel uncertainty estimation and regularization approach for unsupervised domain adaptation in gait recognition, along with a new benchmark dataset.

Findings

Effective in outdoor gait recognition with unlabeled target data

Reduces noise from pseudo labels through uncertainty-based fine-tuning

Outperforms existing methods on the new benchmark

Abstract

Gait recognition is an important AI task, which has been progressed rapidly with the development of deep learning. However, existing learning based gait recognition methods mainly focus on the single domain, especially the constrained laboratory environment. In this paper, we study a new problem of unsupervised domain adaptive gait recognition (UDA-GR), that learns a gait identifier with supervised labels from the indoor scenes (source domain), and is applied to the outdoor wild scenes (target domain). For this purpose, we develop an uncertainty estimation and regularization based UDA-GR method. Specifically, we investigate the characteristic of gaits in the indoor and outdoor scenes, for estimating the gait sample uncertainty, which is used in the unsupervised fine-tuning on the target domain to alleviate the noises of the pseudo labels. We also establish a new benchmark for the proposed problem, experimental results on which show the effectiveness of the proposed method. We will release the benchmark and source code in this work to the public.