Mind the Gap: Bridging Occlusion in Gait Recognition via Residual Gap Correction

TL;DR

This paper introduces RG-Gait, a residual correction method that enhances occluded gait recognition accuracy while maintaining performance on holistic inputs, addressing practical occlusion challenges in real-world scenarios.

Contribution

The paper proposes a novel residual learning approach for occluded gait recognition that does not require paired data and retains holistic recognition performance.

Findings

Significant improvement on Gait3D, GREW, and BRIAR datasets.

Effective residual learning technique for occlusion handling.

Code released publicly for reproducibility.

Abstract

Gait is becoming popular as a method of person re-identification because of its ability to identify people at a distance. However, most current works in gait recognition do not address the practical problem of occlusions. Among those which do, some require paired tuples of occluded and holistic sequences, which are impractical to collect in the real world. Further, these approaches work on occlusions but fail to retain performance on holistic inputs. To address these challenges, we propose RG-Gait, a method for residual correction for occluded gait recognition with holistic retention. We model the problem as a residual learning task, conceptualizing the occluded gait signature as a residual deviation from the holistic gait representation. Our proposed network adaptively integrates the learned residual, significantly improving performance on occluded gait sequences without compromising the holistic recognition accuracy. We evaluate our approach on the challenging Gait3D, GREW and BRIAR datasets and show that learning the residual can be an effective technique to tackle occluded gait recognition with holistic retention. We release our code publicly at https://github.com/Ayush-00/rg-gait.