Application Development for Hand Gestures Recognition with Using a Depth Camera

Autor:	Gulziya Kalymova, Natalya Glazyrina, Dina Satybaldina
Rok vydání:	2020
Předmět:	Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Video processing USB Convolutional neural network law.invention law Gesture recognition Preprocessor RGB color model Computer vision Artificial intelligence business Gesture
Zdroj:	Communications in Computer and Information Science ISBN: 9783030576714 DB&IS
DOI:	10.1007/978-3-030-57672-1_5
Popis:	The aim of the work is to develop an application for hand gestures identification based on a convolutional neural network using the TensorFlow & Keras deep learning frameworks. The gesture recognition system consists of a gesture presentation, a gesture capture device (sensor), the preprocessing and image segmentation algorithms, the features extraction algorithm, and gestures classification. As a sensor, Intel® Real Sense™ depth camera D435 with USB 3.0 support for connecting to a computer was used. For video processing and extraction both RGB images and depth information from the input data, functions from the Intel Real Sense library are applied. For pre-processing and image segmentation algorithms computer vision methods from the OpenCV library are implemented. The subsystem for the features extracting and gestures classification is based on the modified VGG-16, with weights previously trained on the ImageNet database. Performance of the gesture recognition system is evaluated using a custom dataset. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy (99.4%).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7e2c8dcbafedfd3cb6f68e164d5d86b3 https://doi.org/10.1007/978-3-030-57672-1_5 Zobrazit plný text záznamu