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Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility.

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Summary
This summary is machine-generated.

This study developed an Advanced Driver Assistance System (ADAS) for smart electric wheelchairs. The system enhances disabled people's autonomy by detecting and tracking essential indoor objects like doors.

Keywords:
computer visiondeep learningdistance estimationdistance measurementobject detectionobject localizationsemantic mapsmart mobilitytracking

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Area of Science:

  • Robotics
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Smart electric wheelchairs aim to increase independence for disabled individuals.
  • Current systems often lack sophisticated environmental perception capabilities.
  • Assisted navigation requires accurate detection and tracking of key environmental features.

Purpose of the Study:

  • To develop an Advanced Driver Assistance System (ADAS) for smart electric wheelchairs.
  • To enhance the autonomy of disabled people through improved wheelchair navigation.
  • To create a perception layer for detecting and tracking indoor objects like doors and door handles.

Main Methods:

  • Adaptation of the YOLOv3 object detection algorithm for identifying doors and handles.
  • Depth estimation using an Intel RealSense camera.
  • 3D object tracking implemented with the SORT algorithm.

Main Results:

  • Successful detection, depth estimation, and 3D tracking of doors and door handles in an indoor environment.
  • Development of a short-lifespan semantic map for immediate surroundings.
  • Validation through experiments using a custom dataset in a controlled setting.

Conclusions:

  • The developed ADAS provides a crucial perception layer for smart electric wheelchairs.
  • This system significantly contributes to improving the autonomy and navigation capabilities of disabled users.
  • The integration of object detection, depth estimation, and tracking offers a robust solution for indoor wheelchair assistance.