Allen, M., & Taylor, B., & Zhu, P., & Sellards, P. K. (2025, March), Gesture-Based Drone Control: Enhancing Precision with Code Algorithms  Paper presented at 2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia. 10.18260/1-2--54669

\bibitem{asee_peer_54669}
  Allen, M., \& Taylor, B., \& Zhu, P., \& Sellards, P. K. (2025, March), \emph{Gesture-Based Drone Control: Enhancing Precision with Code Algorithms}
  Paper presented at 2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia.
  10.18260/1-2--54669

Mathew Allen, Ben Taylor, Pingping Zhu, and Preston K Sellards.  "Gesture-Based Drone Control: Enhancing Precision with Code Algorithms".  2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia, 2025, March.  ASEE Conferences, 2025.  https://peer.asee.org/54669 Internet.  16 May, 2025

\bibitem{asee_peer_54669}
  Mathew Allen, Ben Taylor, Pingping Zhu, and Preston K Sellards.
  "Gesture-Based Drone Control: Enhancing Precision with Code Algorithms".
  \emph{2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia, 2025, March}.
  ASEE Conferences, 2025.
  https://peer.asee.org/54669 Internet.  16 May, 2025

@INPROCEEDINGS{asee_peer_54669
author = "Mathew Allen, Ben Taylor, Pingping Zhu, and Preston K Sellards"
title = "Gesture-Based Drone Control: Enhancing Precision with Code Algorithms"
booktitle = "2025 ASEE North Central Section  (NCS) Annual Conference"
year = "2025"
month = "March"
address = "Marshall University, Huntington, West Virginia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/54669}
number = {10.18260/1-2--54669}
}

TY  - CPAPER
AB  - This research focuses on advancing the control of drones by utilizing hand gesture recognition through the drone’s camera. By leveraging computer vision algorithms and machine learning techniques implemented in Python, this research aims to enhance the responsiveness and precision of drone operations in various environments. The core objectives involve developing and implementing gesture recognition mechanisms, emphasizing the need for drones to interpret and respond to human gestures accurately. Additionally, integrating real-time video processing to gather information about the operator’s gestures will be crucial for effective control. Thorough testing and algorithm refinements are integral to this research to improve the efficiency and reliability of drone control via hand gestures.
Beyond technological advancements, the significance of this research lies in its potential applications across diverse sectors, such as search and rescue operations, entertainment, and human-computer interaction. The drone control strategies developed in this research hold promise for enhancing user interaction and contributing to more intuitive and accessible drone operations across various industries. These advancements signify progress towards establishing a more user-friendly and versatile autonomous drone landscape, highlighting the research’s implications and practical applications.

AU  - Mathew Allen
AU  - Ben Taylor
AU  - Pingping Zhu
AU  - Preston K Sellards
CY  - Marshall University, Huntington, West Virginia
DA  - 2025/03/28
PB  - ASEE Conferences
TI  - Gesture-Based Drone Control: Enhancing Precision with Code Algorithms
UR  - https://peer.asee.org/54669
DO  - 10.18260/1-2--54669
ER  -