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用于人机交互的视觉手势识别
其他题名Vision-based Hand Gesture Recognition for Human Computer Interaction
单彩峰
学位类型工学硕士
导师谭铁牛
2004-07-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词手势识别 视觉人机交互 视觉跟踪 轨迹分析 服务型机器人 Hand Gesture Recognition Vision-based Human Computer Interaction Visual Tracking Trajectory Analysis Intelligent Service Robots
摘要基于视觉的手势识别,是让计算机能够像人那样看见并理解人的手势;它在人机交 互、虚拟现实、手语理解、远程控制等领域中有着广泛而重要的应用。以智能轮椅作为研 究平台,本文对视觉手势识别在人机交互中的应用做了深入的研究,包括手的特征选取、 手势跟踪、静态手势识别、动态手势理解等方面的内容。本文的主要工作和贡献有: ①提出了一种改进的粒子滤波算法一结合均值漂移的粒子滤波MSEPIF。通过在粒子 滤波中加入均值漂移步骤,MSEPF实现了更为有效的采样策略,提高了粒子集中 的有效粒子数,在一定程度上避免了传统粒子滤波的采样恶化和采样枯竭问题:同 时,MSEPF不需要大量粒子来维持后验概率的多峰分布,节约了所需粒子数,提高 了计算效率。 ②使用MSEPF实现了智能轮椅人机交互场景下的实时手势跟踪。得益于粒子漂 移,MSEPF使用了简单但有效的弱动态模型。我们首先选用肤色对手建模,并 且考虑到动态场景中光照变化会带来肤色改变,在跟踪过程中自适应更新肤色模 型。为了克服背景中存在的肤色干扰,我们提出了融合肤色和运动信息的观测模 型。MSEPF中的粒子漂移也是基于肤色和运动信息进行。 ③基于方向直方图思想,提出使用手轮廓方向直方图来识别静态手势。在借助手势跟 踪算法定位图像中手的位置后,我们根据肤色分割出手的轮廓:然后计算手轮廓方 向直方图,通过与事先训练好的模型匹配来识别手势。 ④通过在时序模板中引入时空轨迹,提出了时序模板轨迹概念,将从运动序列中跟踪 得到的手势运动轨迹压缩到单幅图像中。我们设计了一种两层分类器,通过时序模 板轨迹的形状和运动方向分析,实现对预先定义的七种动态手势的识别。实验表 明,时序模板轨迹优于一般的时序模板,对动态手势具有更好的可分性;且实现简 单,不需要复杂的训练。 ⑤在上述研究的基础上,设计并实现了智能轮椅基于视觉手势识别的外部控制接口, 作为轮椅多模态感知接口的一个重要组成部分。该实时手势控制接口在实际中工作 良好。
其他摘要Vision-based hand gesture recognition, enabling computer to see and understand hand gestures as humans do, could be widely applied in areas such as Virtual Reality, Human Computer Interaction, Sign Language Recognition and Tele-robotics. In the context of an intelligent wheelchair, we conducted research on vision-based hand gesture recognition for human computer interaction. Hand representation, hand tracking, static hand posture recognition, and dynamic hand gesture recognition are discussed here. The main contributions of this thesis are summarized as follows: ①We proposed a novel tracking algorithm, the Mean Shift Embedded Particle Filter (MSEPF), to improve the efficiency of conventional particle filters. By embedding mean shift iteration in particle filter, the MSEPF leads to more efficient sampling, concentrating on particles with large weights; therefore, the degeneracy problem and sampling impoverishment problem of particle filters are circumvented by increasing the number of efficient samples. At the same time, the MSEPF does not need a large number of particles to maintain multiple modes of posterior density, hence save much computation cost. ②Real time hand tracking in intelligent wheelchair environment was achieved by using the MSEPF. A simple but effective dynamic model is utilized here due to particle shifting. We first adopted skin color to represent the hand, and the skin color model is adapted frame-by-frame for skin color could change due to varying illumination. In order to handle the skin-colored distractor in background, we proposed the observation model fusing color and motion cues. Mean shift iteration is also performed on skin color and motion cues. ③Based on the idea of orientation histogram, we proposed orientation his- togram of hand contour to represent hand static posture. After hand is localized in the image, hand contour is obtained by segmentation based on skin color. Orientation histogram of hand contour is computed to match with models learned from training set for final posture recognition. ④We presented the Temporal Template Based Trajectories (TTBT) by introducing spatio-temporal trajectory into temporal templates. TTBT collapse the tracked hand motion trajectory into static image. A two-layer classi- tiers is designed to recognize the predefined seven dynamic gestures, based on the statistical shape and motion orientation analysis of TTBT. TTBT have better separate ability than temporal templates for dynamic gestures. The recognition method is easy to implement and does not need complex training. ⑤By applying the above algorithms on the intelligent wheelchair, we designed and implemented a real time hand control interface, which is part of the multi-modal perceptual interface of the intelligent wheelchair. The human robot interface based on hand gesture recognition developed in this thesis works well in real world.
馆藏号XWLW789
其他标识符789
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6779
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
单彩峰. 用于人机交互的视觉手势识别[D]. 中国科学院自动化研究所. 中国科学院研究生院,2004.
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