Touch Point Prediction for Interactive Public Displays Based on Camera Images
Touch Point Prediction for Interactive Public Displays Based on Camera Images
复制标题
基于摄像头图像的交互式公共显示器的触摸点预测
DOI：
10.1109/cw52790.2021.00029
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Gangyong Jia
中科院分区：
文献类型：
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作者：
Ziwei Song;Yuichiro Kinoshita;Kentaro Go;Gangyong Jia
在使用交互式显示器期间的反馈延迟是目前在人机界面领域正在考虑的问题。有几项研究的重点是使用各种方法减少延迟。提出了一种利用卷积神经网络来预测交互式公共显示的用户触点的框架。该框架在手指到达显示表面之前预测用户触摸事件，以减少反馈的延迟。作为训练数据集，在显示器前收集了18名参与者的1,651个敲击动作。使用收集的敲击动作执行卷积神经网络结构的训练。验证测试结果表明，在触摸显示器之前的390ms处可以达到合理的精度。
Feedback latency during the use of interactive displays is an issue currently being considered in the HCI field. Several studies have focused on reducing latency using various approaches. This paper proposes a framework that uses a convolutional neural network to predict user touch points for interactive public displays. The framework predicts user touch events before the finger reaches the display surface to reduce the latency in feedback. As a training dataset, 1,651 tapping actions were collected from 18 participants in front of a display. The training of the convolutional neural network architecture was performed using the collected tapping actions. Validation test results showed that reasonable accuracy could be achieved at 390 ms before touching the display.