Authors : Dr. Shivani Yadao; Harshita Vyas; A. Likhitha; Pooja Chawan

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/wb9xf7jt

Scribd : https://tinyurl.com/5auvf4jx

DOI : https://doi.org/10.38124/ijisrt/26apr2193

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The increasing need for user-friendly, contactless control systems has drawn a lot of attention to human-drone interaction in recent years. In this paper, a computer vision and machine learning-based hand gesture control system for a quadcopter drone is presented. The suggested system tracks and detects hand landmarks using the MediaPipe framework and records real-time video frames using a webcam. Fist, Open palm, and the directional hand movements are examples of the kind of gesture that is interpreted as flight command by analyzing the spatial locations of the important hand points. The left hand uses the distance between the chosen landmarks for altitude variation, while the right hand is utilized for the direction of the motion. Proportional-derivative (PD) control mechanism and smoothing filters are utilized for stability. Moreover, the hand-locking mechanism for cybersecurity, which checks for the pattern of the gesture, is introduced for secure drone control. Gesture-based takeoff and landing, as well as automatic landing in the absence of hands, are examples of the safety features. The results of the experiment reveal the stability of the drone in indoor space with improved security and gesture recognition.

Keywords : Computer Vision, Drone, Hand Gesture, Machine Learning, Secure.

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