Abstract

This article mainly introduces a gesture control recognition method based on embedded AI computing platform, which uses infrared cameras and AI processing units to recognize and track gestures. Using infrared cameras as detection tools can achieve low-cost, easy-to-use and other characteristics. In this algorithm, firstly, the Gesture recognition method based on color model is used to convert the color of the input gesture image and extract the corresponding skin color features; Then, by weighting the skin color features, richer color information is obtained; Finally, the recognition of gestures was achieved by calculating the similarity between color features and skin tone features. This article designs a gesture control recognition system based on infrared cameras and AI processing units. This article adopts a deep learning based object detection algorithm. The algorithm uses Convolutional Neural Network (CNN) to detect the target, and realizes Gesture recognition and tracking without increasing the hardware cost. In the training process, CNN is used to extract the color features and color information of gesture images for training, while in the testing process, infrared cameras of the same number and size are used to test the trained model. In addition, in order to achieve the characteristics of low cost and ease of use, this paper uses the target detection algorithm based on deep learning to verify the results of Gesture recog-nition.