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Home-Based Monitor for Gait and Activity Analysis
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Human Gait Recognition Based on Self-Adaptive Hidden Markov Model.

Xiuhui Wang, Shiling Feng, Wei Qi Yan

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 6, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel self-adaptive hidden Markov model (SAHMM) for robust human gait recognition. The method improves accuracy despite challenges like changing views and clothing, outperforming existing techniques.

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    Area of Science:

    • Computer Vision
    • Biometrics
    • Machine Learning

    Background:

    • Human gait recognition is crucial for security and surveillance.
    • Existing methods struggle with variations in viewpoint, clothing, and walking speed.
    • Robust gait recognition under diverse conditions remains a significant challenge.

    Purpose of the Study:

    • To propose a novel gait recognition method enhancing accuracy under challenging conditions.
    • To introduce a self-adaptive hidden Markov model (SAHMM) for improved gait analysis.
    • To address limitations of current gait recognition systems.

    Main Methods:

    • Feature extraction using local gait energy images (LGEI) to create observation vectors.
    • Optimization of SAHMM parameters using the extracted feature set.
    • Extensive evaluation on CASIA Dataset B and OU-ISIR Large Population Dataset.

    Main Results:

    • The proposed SAHMM method demonstrates superior performance in gait recognition.
    • Effective handling of variations including cross-view, human dressing, and bag carrying.
    • Verified generalization ability across different large-scale datasets.

    Conclusions:

    • The SAHMM-based gait recognition method offers enhanced accuracy and robustness.
    • This approach effectively overcomes common challenges in real-world gait analysis.
    • The method shows significant potential for practical biometric applications.