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Related Experiment Video

Updated: Dec 27, 2025

Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds

Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds

Published on: August 25, 2020

MuPeG-The Multiple Person Gait Framework.

Rubén Delgado-Escaño¹, Francisco M Castro¹, Julián R Cózar¹

¹Department of Computer Architecture, University of Málaga, 29071 Málaga, Spain.

Sensors (Basel, Switzerland)

|March 4, 2020

Summary

This summary is machine-generated.

Gait recognition struggles with multiple people due to occlusions. A new framework, MuPeG, generates realistic multi-subject datasets, revealing significant accuracy drops, highlighting the need for advanced models for real-world identification.

Keywords:

gait recognition, gait framework, gait dataset, multiple subjects, augmented dataset

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Last Updated: Dec 27, 2025

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Published on: August 25, 2020

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Area of Science:

Computer Vision
Biometrics
Pattern Recognition

Background:

Gait recognition offers uncooperative subject identification.
Current datasets yield high accuracy (>90%) but lack real-world complexity.
Existing datasets typically feature only one subject, limiting applicability.

Purpose of the Study:

To address the limitations of single-subject datasets in gait recognition.
To introduce a framework (MuPeG) for generating multi-subject augmented datasets.
To propose an experimental methodology for evaluating gait recognition in complex, multi-subject scenarios.

Main Methods:

Developed the MuPeG framework to automatically generate multi-subject datasets from existing ones.
Utilized MuPeG to create augmented versions of TUM-GAID and CASIA-B datasets.
Conducted experiments on both original (single-subject) and augmented (multi-subject) datasets.

Main Results:

Accuracy dropped significantly on augmented datasets (54.8% for TUM-GAID, 42.3% for CASIA-B) compared to original datasets (99.7% and 98.0%).
Demonstrated the substantial increase in difficulty for gait recognition with multiple subjects present.
Highlighted the inadequacy of current models trained on single-subject data for real-world scenarios.

Conclusions:

The MuPeG framework effectively generates challenging, realistic multi-subject datasets for gait recognition research.
Significant performance degradation underscores the need for new models and methodologies.
The findings pave the way for more robust gait recognition systems applicable in crowded environments.