Application Controlling Using Hand Gestures Through Yolov5s

With the aid of Python, OpenCV, Yolov5s, and PyAutoGui, we attempt to use hand gestures for controlling an application. The need for unfettered interaction has made it difficult for conventional input devices like the keyboard and mouse to be used. In linguistics, gestures with the hand are a crucial part of body language (Nagalapuram GD, Roopashree S, Varshashree D, Dheeraj D, Nazareth DJ, Controlling media player with hand gestures using convolutional neural network. In: 2021 IEEE Mysore sub section international conference (MysuruCon). 978-1-6654-3888-9/21/$31.00 ©2021 IEEE, https://doi.org/10.1109/MYSURUCON52639.2021.9641567 , 2021). Using the hand as a device makes it simple to interface with humans and computers. It would be intriguing to engage with machines using hand gestures (Li G, Li D, Yang A, Real-time hand gesture detection based on Yolov5s. In: Proceedings of the 41st Chinese control conference, July 25–27, Hefei, China, 2022). Different applications, Various software programs, including Media Player, PowerPoint, PDF viewers, and software that accepts keyboard and mouse input, are controlled by hand gestures. Interaction is simple, practical, and requires no tedious additional equipment when gestures are used (Paliwal M, Sharma G, Nath D, Rathore A, Mishra H, Mondal S, A dynamic hand gesture recognition system for controlling VLC media player. In: 2013 international conference on advances in technology and engineering (ICATE). IEEE, 2013). A camera-based hand detection system that employs a live webcam stream to identify the gesture once it has been observed. This system gives the user access to essential keyboard functions with the aid of the PyAutoGui package. With this technology, we can increase the usability of computers for people with mobility issues or who cannot have physical contact with peripherals. A deep learning algorithm called Yolov5s is used for object detection. The precision and recall are near to 1, the mAP (0.5) value for hand gesture recognition is 0.995, and the mAP (0.5: 0.95) value is 0.978. The Yolov5 model may more effectively fulfill the demands of real-time gesture recognition and can serve as a crucial practical foundation and point of reference for advancing human–computer interaction technology in the future (Jalab HA, Omer HK, Human computer interface using hand gesture recognition based on neural network. In 2015 5th national symposium on information technology: towards new smart world (NSITNSW), 2015).