一种面向实时交互的变形手势跟踪方法

变形手势跟踪是基于视觉的人机交互研究中的一项重要内容.单摄像头条件下,提出一种新颖的变形手势实时跟踪方法.利用一组2D手势模型替代高维度的3D手模型.首先利用贝叶斯分类器对静态手势进行识别,然后对图像进行手指和指尖定位,通过将图像特征与识别结果进行匹配,实现了跟踪过程的自动初始化.提出将K-means聚类算法与粒子滤波相结合,用于解决多手指跟踪问题中手指互相干扰的问题.跟踪过程中进行跟踪状态检测,实现了自动恢复跟踪及手势模型更新.实验结果表明,该方法可以实现对变形手势快速、准确的连续跟踪,能够满足基于视觉的实时人机交互的要求.

An Approach to Tracking Deformable Hand Gesture for Real-Time Interaction

The tracking of deformable hand gesture is a very important task in vision-based HCI (human-computer interaction) research. A novel real-time tracking approach is proposed to capture the motion of deformable hand gesture with single camera. The proposed approach uses a set of 2D hand models in place of high-dimensional 3D model. It achieves auto-initialization by firstly using Bayesian classifier to do posture recognition,and then locating fingers and fingertips to fit image features to recognized posture. It solves the problem of interference among fingers during tracking successfully by the integration of K-means clustering and particle filter. Moreover,a state checking process is embedded into tracking method,and it realizes resumption from tracking failure and update of hand models automatically. Experimental results show that the proposed method can achieve continuous real-time tracking of deformable hand gesture with high precision,and thus it can meet the requirements from real-time vision-based human-computer interaction.