@inproceedings{a915ee5e6f6647d09cf3062e641a9995,
title = "Gesture Intention Understanding Based on Depth and RGB Data",
abstract = "Aiming at the problem that the process of gesture recognition based on color image is greatly affected by environmental factors such as lighting, a gesture intent understanding method based on the fusion of Red-Green-Blue (RGB) data and depth data is proposed. Firstly, the gesture feature extraction based on the Speeded Up Robust Feature (SURF) method after foreground segmentation are used to get gesture information. Then, we apply Backpropagation (BP) neural network to classify and recognize gestures. The final recognition results are obtained through data fusion from recognition results based on both RGB images and depth images. We evaluated the effectiveness of the proposed method through ChaLearn Gesture Database.",
keywords = "Depth Data, Gesture Intention Understanding, RGB Data",
author = "Yu Feng and Luefeng Chcn and Wanjuan Su and Kaoru Hirota",
note = "Publisher Copyright: {\textcopyright} 2018 Technical Committee on Control Theory, Chinese Association of Automation.; 37th Chinese Control Conference, CCC 2018 ; Conference date: 25-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "5",
doi = "10.23919/ChiCC.2018.8483387",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "9556--9559",
editor = "Xin Chen and Qianchuan Zhao",
booktitle = "Proceedings of the 37th Chinese Control Conference, CCC 2018",
address = "United States",
}