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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Exploring Self-Supervised Vision Transformers for Gait Recognition in the Wild.

Sensors (Basel, Switzerland)·2023
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WildGait: Learning Gait Representations from Raw Surveillance Streams.

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Updated: Aug 27, 2025

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
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Learning Gait Representations with Noisy Multi-Task Learning.

Adrian Cosma1, Emilian Radoi1

  • 1Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, 006042 Bucharest, Romania.

Sensors (Basel, Switzerland)
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces DenseGait, a large dataset for gait analysis, and GaitFormer, a model that identifies people and pedestrian attributes using only movement patterns. GaitFormer achieves high accuracy without manual annotations, advancing biometric identification.

Keywords:
gait recognitionmulti-task learningpose estimationself-supervised learningweakly-supervised learning

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Area of Science:

  • Computer Vision
  • Biometrics
  • Machine Learning

Background:

  • Gait analysis offers a non-cooperative biometric for person identification.
  • Existing gait analysis primarily focuses on identification and demographics, neglecting appearance attributes.
  • Appearance-based methods often rely on cooperative subjects or specific conditions.

Purpose of the Study:

  • To explore pedestrian attribute identification solely from movement patterns.
  • To introduce DenseGait, the largest dataset for pretraining gait analysis systems.
  • To develop and evaluate GaitFormer, a transformer-based model for multi-task gait analysis.

Main Methods:

  • Construction of DenseGait dataset with 217K anonymized tracklets, automatically annotated with 42 appearance attributes.
  • Development of GaitFormer, a transformer-based model leveraging multi-task pretraining on DenseGait.
  • Evaluation of GaitFormer on benchmark datasets (CASIA-B, FVG) for person and attribute identification.

Main Results:

  • GaitFormer achieved 92.5% accuracy on CASIA-B and 85.33% on FVG.
  • Significant accuracy improvements (+14.2% on CASIA-B, +9.67% on FVG) compared to similar methods.
  • Successful identification of gender and various appearance attributes using only movement patterns.

Conclusions:

  • DenseGait provides a comprehensive resource for advancing gait analysis research.
  • GaitFormer demonstrates the potential of transformer models for multi-task gait analysis.
  • Movement patterns alone are sufficient for accurate person and attribute identification, offering new possibilities for surveillance and biometrics.