Исследование сверточной нейронной сети небольшой архитектуры для распознавания жестов

Abstract

Currently, more research is aimed at solving problems using computer vision and artificial intelligence. Most frequent are solutions and approaches using gesture recognition based on infrared sensors or neural networks. The relevance of the subject matter is due to the possibility of applying the proposed approach for managing the operation of objects without tactile contact and voice identification of commands, as well as its simplicity from the point of view of the end-user. This paper proposes a proprietary convolutional neural network architecture to solve gesture classification. The accuracy of the network operation was evaluated depending on the distance between the camera and the hand, as well as depending on the complexity of the gesture.