HGM-4

Gesture recognition technology is rapidly growing in the recent years due to the demands of many application such as computer game and sport, human robot interaction, assistant systems, sign language interpretation and e-commerce. One of the most important of gesture recognition is hand-gesture recognition. For example, it can be used to control all devices (television, radio, air-condition, and doors) by just hand gestures for smart home application. The HGM-4 dataset is built for hand gesture recognition (the full dataset is available from: http://dx.doi.org/10.17632/jzy8zngkbg.2) which contains total 4,160 colour images (1280 × 700 pixels) of 26 hand gestures captured by four cameras at different position. The training and testing set are defined to create a benchmark framework for comparing the experimental results.近年来，受电子游戏与体育、人机交互、辅助系统、手语翻译及电子商务等多领域应用需求的驱动，手势识别技术正快速发展。而手势识别中最为关键的分支之一便是手部手势识别。例如在智能家居场景中，仅通过手部手势即可操控电视、收音机、空调与门窗等各类设备。本次构建的HGM-4数据集专为手部手势识别任务打造（完整数据集可通过http://dx.doi.org/10.17632/jzy8zngkbg.2获取），包含由4台不同位置摄像头采集的26种手部手势的共计4160张彩色图像，图像分辨率为1280×700像素。该数据集已预先划分训练集与测试集，旨在构建可用于实验结果对比的标准化基准框架。