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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    Title :

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


    Additional title:

    Springer Proceedings Phys.



    Conference:

    International Seminar on Aerospace Science and Technology ; 2024 ; Bali, Indonesia September 17, 2024 - September 17, 2024



    Publication date :

    2025-02-15


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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