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.
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