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Related Experiment Video

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Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method.

Li Wang1, Yajun Li2, Fei Xiong3

  • 1School of Physical Education, Sichuan Normal University, Chengdu 610101, China.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel gait recognition system using optical motion capture data. The method achieves 100% accuracy for human identification, enabling multi-player support in surveillance and authentication.

Keywords:
decision fusiongait recognitionkernel ELMoptical motion capturesensor fusion

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Area of Science:

  • Biometrics and Human-Computer Interaction
  • Computer Vision and Pattern Recognition

Background:

  • Human identification using motion capture data is crucial for security applications.
  • Optical Motion Capture Systems (OMCS) offer high-precision 3D tracking but haven't been fully explored for gait recognition.
  • Current OMCS typically support single-player scenarios, limiting efficiency.

Purpose of the Study:

  • To investigate the performance of OMCS for gait recognition.
  • To develop an OMCS-based gait recognition system capable of supporting multiple individuals simultaneously.
  • To evaluate a novel decision fusion method for enhanced accuracy.

Main Methods:

  • A four-step gait recognition method: feature extraction, unreliable feature calibration, single motion frame classification, and decision fusion.
  • Utilizing Kernel Extreme Learning Machine (KELM) for single motion classification.
  • Proposing a Reliability Weighted Sum (RWS) decision fusion method for combining frame decisions.

Main Results:

  • KELM outperformed Support Vector Machine (SVM) and Random Forest in single motion frame classification.
  • The proposed RWS decision fusion achieved superior accuracy compared to conventional fusion rules.
  • With 10 lower-body trackers, 100% validation accuracy was achieved using fewer than 50 gait frames.

Conclusions:

  • OMCS are effective for high-accuracy gait recognition.
  • The proposed KELM and RWS fusion method significantly enhance gait recognition performance.
  • The developed system supports multi-player identification, improving OMCS efficiency for security applications.