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Exploring Self-Supervised Vision Transformers for Gait Recognition in the Wild.

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WildGait: Learning Gait Representations from Raw Surveillance Streams.

Adrian Cosma1, Ion Emilian Radoi1

  • 1Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, 060042 Bucharest, Romania.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary

This study introduces WildGait, a novel self-supervised learning framework for gait recognition in real-world scenarios. It enables accurate person identification using only motion data, even with single, uncooperative walking instances.

Keywords:
gait recognitiongraph neural networkspose estimationself-supervised learning

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Area of Science:

  • Computer Science
  • Biometrics
  • Machine Learning

Background:

  • Gait recognition offers non-invasive person identification but traditional methods require controlled environments.
  • Real-world surveillance presents challenges like multiple individuals and single-pass encounters.
  • Existing methods often rely on cooperative scenarios, limiting practical application.

Purpose of the Study:

  • To develop a gait recognition framework for unconstrained, real-world scenarios.
  • To address privacy concerns by focusing solely on motion information.
  • To improve gait recognition accuracy with limited annotated data.

Main Methods:

  • Proposed WildGait, a self-supervised learning framework using Spatio-Temporal Graph Convolutional Networks.
  • Pre-trained the network on a large dataset of automatically annotated, anonymized 2D skeleton sequences (Uncooperative Wild Gait dataset).
  • Utilized motion information from walking individuals, excluding appearance-based data.

Main Results:

  • Achieved state-of-the-art performance in pose-based gait recognition.
  • Demonstrated reliable gait recognition in unconstrained environments.
  • Successfully learned gait signatures from a large dataset of anonymized walking skeletons.

Conclusions:

  • WildGait offers a robust solution for gait recognition in challenging, real-world surveillance.
  • The Uncooperative Wild Gait dataset advances research in unconstrained biometrics.
  • Self-supervised learning effectively addresses data scarcity in gait recognition applications.