IEEE Trans Vis Comput Graph. 2022 Nov;28(11):3618-3628. doi: 10.1109/TVCG.2022.3203004. Epub 2022 Oct 21.
In this paper we examine the task of key gesture spotting: accurate and timely online recognition of hand gestures. We specifically seek to address two key challenges faced by developers when integrating key gesture spotting functionality into their applications. These are: i) achieving high accuracy and zero or negative activation lag with single-time activation; and ii) avoiding the requirement for deep domain expertise in machine learning. We address the first challenge by proposing a key gesture spotting architecture consisting of a novel gesture classifier model and a novel single-time activation algorithm. This key gesture spotting architecture was evaluated on four separate hand skeleton gesture datasets, and achieved high recognition accuracy with early detection. We address the second challenge by encapsulating different data processing and augmentation strategies, as well as the proposed key gesture spotting architecture, into a graphical user interface and an application programming interface. Two user studies demonstrate that developers are able to efficiently construct custom recognizers using both the graphical user interface and the application programming interface.
在本文中，我们研究了关键手势检测任务：准确和及时地在线识别手势。我们特别旨在解决开发人员在将关键手势检测功能集成到其应用程序中时面临的两个关键挑战。这些是：i）实现单次激活时的高精度和零或负激活延迟；和 ii）避免对机器学习的深度学习专业知识的要求。我们通过提出由新颖的手势分类器模型和新颖的单次激活算法组成的关键手势检测架构来解决第一个挑战。该关键手势检测架构在四个单独的手部骨骼手势数据集上进行了评估，实现了早期检测的高精度识别。我们通过将不同的数据处理和增强策略以及所提出的关键手势检测架构封装到图形用户界面和应用程序编程接口中来解决第二个挑战。两个用户研究表明，开发人员能够使用图形用户界面和应用程序编程接口高效地构建自定义识别器。
