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Related Experiment Video

Updated: Jul 25, 2025

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

Regional Time-Series Coding Network and Multi-View Image Generation Network for Short-Time Gait Recognition.

Wenhao Sun^1,2, Guangda Lu^1,2, Zhuangzhuang Zhao^1,2

¹School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China.

Entropy (Basel, Switzerland)

|June 28, 2023

Summary

This study introduces a novel gait recognition network that generates cross-view gait data and extracts motion features. The method effectively recognizes individuals from short gait videos, enhancing biometric authentication.

Keywords:

feature fusion image generation networks short-time gait recognition time-series feature extraction

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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

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Last Updated: Jul 25, 2025

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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Area of Science:

Biometric Authentication
Computer Vision
Human Motion Analysis

Background:

Gait recognition is crucial for biometrics but hindered by short data and view variations.
Existing methods struggle with incomplete gait sequences and cross-view inconsistencies.

Purpose of the Study:

To develop a gait recognition system that overcomes limitations of short data and cross-view variations.
To enhance the accuracy and robustness of gait recognition using novel feature extraction techniques.

Main Methods:

A gait data generation network expands cross-view gait silhouette data.
A gait motion feature extraction network uses regional time-series coding for joint motion analysis.
Bilinear matrix decomposition pooling fuses silhouette and motion features.

Main Results:

The proposed network effectively expands multi-view gait data and extracts human motion time-series features.
Validation on OUMVLP-Pose and CASIA-B datasets demonstrated network effectiveness using metrics like Rank-1 accuracy.
Real-world tests confirmed the method's feasibility and effectiveness for short-time video gait recognition.

Conclusions:

The developed two-branch fusion network achieves complete gait recognition from short video inputs.
The approach successfully addresses challenges in gait recognition, offering a robust and feasible solution.