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Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds
Published on: August 25, 2020
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Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction
1School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
Sensors (Basel, Switzerland)
|August 26, 2023
Summary
This study introduces a novel gait recognition method using human keypoint extraction for frame-level identification, even with discontinuous images. The enhanced model achieves high accuracy, demonstrating robustness with limited training data.
Area of Science:
- Computer Science
- Biometrics
- Artificial Intelligence
Background:
- Gait recognition leverages unique walking patterns for identification, offering advantages like long-range detection and subject cooperation independence.
- Existing methods often struggle with discontinuous image sequences and require extensive training data.
Purpose of the Study:
- To propose a robust gait recognition method capable of frame-level identification from discontinuous image sequences.
- To enhance feature extraction by minimizing reliance on temporal image characteristics and reducing estimation errors.
Main Methods:
- Human keypoint extraction for gait analysis.
- A discontinuous frame screening module to filter input image data.
- Integration of Cross-Stage Partial (CSP) connections and XBNBlock within a ResGCN spatial-temporal graph convolutional network for feature extraction.
Main Results:
- Achieved an average recognition accuracy of 79.5% on the CASIA-B gait dataset.
- Demonstrated robustness with 78.1% accuracy on CASIA-B using limited training frames.
Conclusions:
- The proposed method effectively recognizes gait from frame-level, discontinuous images.
- The integration of CSP and XBNBlock enhances feature extraction and model robustness, particularly with limited data.
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