Deep Learning based Gesture Recognition for Drones

Min-Fan Ricky Lee, C. Chung, Adalberto Sergio Montania Espinola, Marcelo Javier Gómez Vera, Guillermo Federico Pallares Caballero
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Abstract

With the development of UAV, more and more people use the lens of drones for image recognition. Among them, the application of gesture recognition does not systematically sort out relevant information, and for gesture recognition, its accuracy will be a problem. In addition, during the recognition process, different scenes will also affect the judgment result of the gesture, so how to achieve the reduction of the interference brought by the venue is also a challenge. This paper summarizes recent papers on the application of gesture recognition on drones and applies it to gesture recognition. For the training module, the RNN and CNN architecture will be used for training. For the interference caused by the environment, we add more environment maps for the training. This paper summarizes the recent gesture recognition solutions and applies them to gesture recognition to improve the accuracy of gesture recognition, which will provide an additional option for gesture recognition control.
基于深度学习的无人机手势识别
随着无人机的发展,越来越多的人使用无人机的镜头进行图像识别。其中,手势识别的应用并没有对相关信息进行系统的梳理,对于手势识别来说,其准确性将是一个问题。此外,在识别过程中,不同的场景也会影响手势的判断结果,因此如何实现减少场地带来的干扰也是一个挑战。本文总结了近年来手势识别技术在无人机上的应用,并将其应用到手势识别中。对于训练模块,将使用RNN和CNN架构进行训练。针对环境的干扰,我们增加了更多的环境图进行训练。本文总结了最近的手势识别解决方案,并将其应用于手势识别,以提高手势识别的准确性,为手势识别控制提供了一个额外的选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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