Hand gesture tracking algorithm based on visual attention

In the majority of the interaction process, the operator often focuses on the tracked 3D hand gesture model at the "interaction points" in the collision detectionscene, such as "grasp" and "release" and objects in the scene, without paying attention to the tracked 3D hand gesture model in the total procedure. Thus in this paper, a visual attention distribution model of operator in the "grasp", "translation", "release" and other basic operation procedures is first studied and a 3D hand gesture tracking algorithm based on this distribution model is proposed. Utilizing the algorithm, in the period with a low degree of visual attention, a pre-stored 3D hand gesture animation can be used to directly visualise a 3D hand gesture model in the interactive scene; in the time period with a high degree of visual attention, an existing "frame-by-frame tracking" approach can be adopted to obtain a 3D gesture model. The results demonstrate that the proposed method can achieve real-time tracking of 3D hand gestures with an effective improvement on the efficiency, fluency, and availability of 3D hand gesture interaction.     

人手粒子滤波跟踪器的研究与设计

提出一种粒子滤波方法中粒子样本采样方法,旨在用较少的粒子数描述高维状态变量的后验概率分布.首先,研究特定人机交互条件下操作者的认知心理特点和人手运动特点,在此基础上提出了状态变量微观结构的基本概念;然后,进一步探讨描述微观结构的一般方法;最后,提出了基于状态变量微观结构的粒子采样方法和改进的粒子滤波跟踪算法.状态变量的微观结构为采样算法的设计提供了一种统一、高效的数学模型,以此为基础的采样算法可以有效避免对质量比较差的粒子样本进行大量采样.为了验证算法的有效性和性能,进行了大量实验,结果表明,与传统的粒子滤波方法相比,采用少量的粒子样本就可以达到较高的跟踪精度.     

基于用户行为分析的手势跟踪方法

人手结构的高维性而导致粒子滤波跟踪方法中采样数目非常庞大,是实现运动人手的实时性跟踪研究的主要障碍之一.以降低粒子数目为目标,以行为分析和建模为切入点,提出一种手势跟踪方法.首先分析操作者在手势操作过程中的行为特征,建立人手运动的动态模型;其次,研究动态模型的基本特征,并给出一种描述方法;然后,建立人手运动的时段模型,分析了手势状态的时间-空间关系.在此基础上,提出了状态变量微观结构的概念,重点给出了基于状态变量微观结构的手势跟踪算法;最后,设计和完成了实验,并与相关参考文献方法的实验结果进行对比.结果表明,采用该算法,用少量粒子就可以得到比较精确的跟踪结果.提出的核心算法已经用于一个基于自然手势交互的三维虚拟装配原型系统.