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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Real-time oriented behavior-driven 3D freehand tracking for direct interaction
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Real-time oriented behavior-driven 3D freehand tracking for direct interaction

机译:直接交互的实时面向行为驱动的3D写意跟踪

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摘要

Articulated hand tracking systems have been commonly used in virtual reality applications, including selection-move-release systems that use freehand as input device. However, building an effective real-time hand tracker remains a challenge. Motivated by the need for three-dimensional(3D)freehand-based human-computer interface in the selection-move-release systems, the current study aims to develop 3D freehand tracking, in which the high dimensionality of articulated hand models is one of the dominating obstacles for tracking 3D hand in real-time. This study focuses on forming the behavioral model for users in selection-move-release systems to build a natural, direct, and effective human-computer interface. First, the statistical models for users were learned. Second, the behavioral models were derived from the statistical models to form a general behavioral model database. Third, a freehand tracking algorithm for interaction between human and computer was presented based on the behavioral models. Fourth, the proposed approach was tested in several real selection-move-release systems, and the experimental results were provided. Unlike previous studies on this subject, the proposed behavioral model was composed of Correlation among Local Motion Models and cognitive information. Experimental results show that the proposed algorithm can achieve satisfactory results in relation to time cost and accuracy compared with some previous studies.
机译:铰接式的手动跟踪系统通常用于虚拟现实应用中,包括使用手法作为输入设备的选择 - 移动版本系统。但是,建立有效的实时手持式追踪器仍然是一个挑战。通过对基于三维(3D)的自由手的人机界面在选择 - 移动释放系统中,目前的研究旨在开发3D写手跟踪,其中铰接手机模型的高维度是其中之一在实时占据跟踪3D手的障碍。本研究侧重于为选择 - 移动释放系统中的用户形成行为模型,以构建自然,直接和有效的人机界面。首先,学习了用户的统计模型。其次,行为模型源自统计模型以形成一般行为模型数据库。第三,基于行为模型提出了一种用于人与计算机交互的写意跟踪算法。第四,在几种真正的选择 - 移动释放系统中测试了所提出的方法,提供了实验结果。与以前的对该受试者的研究不同,所提出的行为模型由本地运动模型和认知信息之间的相关性组成。实验结果表明,与一些先前的研究相比,该算法可以实现与时间成本和准确性相关的令人满意的结果。

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