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This research aimed to develop an indoor drone guidance system that relies on human hand gestures for control, as Global Positioning System (GPS) is unavailable indoors. A computer vision system was implemented using Mediapipe and a Convolutional Neural Network (CNN) to recognize and interpret hand gestures captured by a drone camera. The system was trained on a dataset of 10 distinct hand gestures, achieving a 95% accuracy rate with rapid processing time. Real-world tests were conducted using a DJI Tello drone. The developed system provides a user-friendly, accessible method for controlling drones indoors, requiring no prior experience.

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Computer Vision Implementation in a Hand Gesture-Based Indoor Drone Guidance System

  • 会议论文

  • 发表日期：

    2025年

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  • Javensius Sembiring,
  • Adhitya Rizky Andhira Kusumah,
  • Lalu Muhamad Alhadad,
  • Patrisius Bagus Alvito Baylon,
  • Yazdi Ibrahim Jenie,
  • Hari Muhammad

摘要

This research aimed to develop an indoor drone guidance system that relies on human hand gestures for control, as Global Positioning System (GPS) is unavailable indoors. A computer vision system was implemented using Mediapipe and a Convolutional Neural Network (CNN) to recognize and interpret hand gestures captured by a drone camera. The system was trained on a dataset of 10 distinct hand gestures, achieving a 95% accuracy rate with rapid processing time. Real-world tests were conducted using a DJI Tello drone. The developed system provides a user-friendly, accessible method for controlling drones indoors, requiring no prior experience.