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Wheelchair detection using cascaded decision tree.

Chun-Rong Huang1, Pau-Choo Julia Chung, Kuo-Wei Lin

  • 1Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan,. nckuos@iis.sinica.edu.tw

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|December 17, 2009
PubMed
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This study introduces an efficient wheelchair detector for healthcare systems. The system accurately identifies wheelchairs and their movement direction using a cascaded decision tree, improving patient monitoring.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Healthcare Technology

Background:

  • Healthcare systems aim for automated patient monitoring, especially for those with special needs.
  • Caregiver assistance can be improved through timely alerts for patient movement.
  • Existing methods may lack efficiency or accuracy in complex environments.

Purpose of the Study:

  • To develop an efficient single-camera detector for multidirectional wheelchairs.
  • To simultaneously detect wheelchairs and their movement direction from video frames.
  • To enhance patient monitoring systems for individuals requiring special assistance.

Main Methods:

  • Proposed an efficient single-camera multidirectional wheelchair detector.
  • Utilized a novel cascaded decision tree (CDT) approach.

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  • Combined decision tree structure with boosted-cascade classifiers for hierarchical decision-making.
  • Integrated tracking history to guide detection routes within the CDT.

    • Main Results:

    • Achieved simultaneous detection of wheelchairs and their movement direction.
    • Demonstrated rapid rejection of non-wheelchair objects through hierarchical decisions.
    • Reduced detection time and increased accuracy by guiding detection routes.
    • Reported over 92% detection rate in cluttered scenes.

    Conclusions:

    • The proposed CDT-based wheelchair detector is efficient and accurate for healthcare systems.
    • This technology can significantly improve automated patient monitoring and caregiver assistance.
    • The method shows promise for real-time applications in complex, real-world environments.