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Using end-to-end learning and PyAutoGUI to apply gesture recognition for human-computer interaction
- 1 Shandong University
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Abstract
Contact-less human-machine interaction is becoming increasingly important due to the growing number of special environmental needs and accessibility situations. Gesture recognition has also been a hot topic in computer vision and machine learning in recent years. In this paper, a real-time computer manipulation system based on hand gesture recognition is studied and deployed. A relatively mature end-to-end target recognition model, the YOLOv5 model, is trained in this paper to achieve real-time detection and recognition of hand gestures. According to the result of the recognition, it is translated into the corresponding operation on the computer according to a set of rules, and then PyAutoGUI is used to actually control the computer. At the end of the research, the trained YOLOv5 model exhibited excellent performance and verified the feasibility and scalability of the solution. This is a good inspiration for developing a more convenient and efficient related software.
Keywords
computer vision; end-to-end learning; hand gesture recognition; YOLOv5; human-computer interaction
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Cite this article
Tang,J. (2023). Using end-to-end learning and PyAutoGUI to apply gesture recognition for human-computer interaction. Applied and Computational Engineering,15,141-148.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Computing and Data Science
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