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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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Free-view gait recognition.

Yonghong Tian1,2, Lan Wei1, Shijian Lu3,2

  • 1National Engineering Laboratory for Video Technology, School of Electronics Engineering and Computer Sciences, Peking University, Haidian, China.

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|April 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel view-adaptive mapping (VAM) approach for accurate person identification using human gait recognition, even with unknown or changing camera angles. The VAM method significantly improves free-view gait recognition performance in real-world scenarios.

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Human gait is a reliable biometric for remote identification.
  • Varying view angles present a significant challenge for gait recognition accuracy.
  • Existing View Transformation Models (VTM) are ineffective in uncontrolled environments with unknown or dynamic view angles.

Purpose of the Study:

  • To develop a robust method for free-view gait recognition.
  • To overcome the limitations of traditional VTM in uncontrolled environments.
  • To enable accurate person identification from gait sequences captured at arbitrary view angles.

Main Methods:

  • Proposed a novel view-adaptive mapping (VAM) approach.
  • Employed walking trajectory fitting (WTF) to estimate gait sequence view angles.
  • Utilized a joint gait manifold (JGM) for optimal manifold learning between probe and gallery data.
  • Incorporated a RankSVM-based algorithm to supplement gallery data.

Main Results:

  • The VAM approach demonstrated superior performance compared to existing methods.
  • Experiments on indoor and outdoor datasets validated the effectiveness of VAM.
  • The method successfully addresses the challenges of unknown and dynamic view angles in gait recognition.

Conclusions:

  • The proposed VAM approach offers a significant advancement in free-view gait recognition.
  • This method enhances the reliability of gait biometrics in real-world, uncontrolled scenarios.
  • VAM provides a practical solution for person identification using gait under varying viewing conditions.