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复杂背景下手的检测、跟踪与识别
其他题名Hand Detection, Tracking and Recognition in Real Scene
陈小鹿
学位类型工学硕士
导师王阳生
2008-06-03
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
学位专业计算机应用技术
关键词人机手势交互 Adaboost 粒子滤波 主动形状模型 Hausdorff距离 Gesture Based Human Computer Interaction Adaboost Particle Filter Active Shape Models Hausdorff Distance
摘要随着计算机技术的发展,传统的人机交互技术已难以适应越来越复杂多样的需求。用户要求更加自然和智能的交互方法,包括声音、视觉和智能传感器等等。其中基于计算机视觉的方法具有自然、方便的特点,受到了广泛的关注。手势更是其中最具有自然交互性的方式,目前在人机交互、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。 复杂背景下人手的检测、跟踪和识别研究有着重要的学术价值和广泛的应用前景。本文以实现自然背景下鲁棒的人机手势交互为目标,针对此目标涉及的人手的检测、跟踪和手势的识别进行了深入的研究,并取得了一定的研究成果。本文的主要工作及贡献概述如下:  把人脸检测中广泛采用的Adaboost统计方法用于手掌的定位,因为每个人的手掌都有明显的三条长线,可以作为特征进行人手的检测。Adaboost方法检测的优点是速度快且准确率高,通过采用积分图像来快速计算的简单特征以及分类器的级联结构保证了算法的实时性。  采用高斯建模的方法对肤色空间在规范化的RGB通道上建模,并采用自适应阈值的方法去除光线和背景变化的影响,效果良好,能在90%的情况下达到理想的肤色提取效果。  提出采用粒子滤波和主动形状模型相结合的方法进行手的运动的跟踪,结合了两种方法的优点,克服了各自的不足。粒子滤波是基于粒子集传播的迭代贝叶斯滤波,能够同时维持多峰分布,可以被有效地用于各种非线性和非高斯问题的估计中;采用主动形状模型表示人手,可以准确地表示出手的各种形状。本文先用粒子滤波对手的整体运动做跟踪,然后在此结果上利用主动形状模型进行手的具体的形状的调节,能够准确有效地跟踪手的各种运动,包括:手整体的平移、旋转、放缩,和单独某个手指的伸缩、旋转等运动。  利用跟踪结果进行动态手势识别;对现有的Hausdorff距离进行改进,使之适用于噪声较多的情况下的物体相似度度量,并利用改进方法进行视频中简单静态手势“剪刀、石头、布”的识别,效果良好。
其他摘要With the development of computer sciences, the traditional Human Computer Interface (HCI) has become limited for the more and more complicated requirements. Hence, the natural and intelligent interactive means, such as voice, vision and advanced sensor based methods, have emerged as the time require. The computer vision based method is getting more and more attention for its naturalness and convenience. Moreover, hand gesture is one of the most interactive means that has been widely applied in areas such as Virtual Reality, Human Computer Interaction, Sign Language Recognition and Tele –robotics. The research on hand detection, tracking and gesture recognition in real scene has both significant academic importance and broad applications. With the aim of designing a robust Human-Computer interaction system, we focus our research on hand detection, tracking and gesture recognition, the main contributions are summarized as follows:  The Adaboost statistical method, which was applied widely in face detection, was used to palm detection. Each palm has three long clear lines, which can be used as feature for detection. The advantage of this method is high speed and high accuracy. Through integral image to calculate simple feature and the cascade structure of the classifier ensure that the method work in real time.  By modeling skin color space in normalized RGB space based on Gaussian model and utilizing an adaptive threshold method to remove the influence of the changing light and background, we can get the perfect skin color extraction effect in ninety percent condition.  We proposed the method combining particle filter and Active Shape Models together to track the moving hand. This method units the advantage of each method and overcome their disadvantages. Particle filter, a recursive Bayesian filter based on particle set propagation, can maintain multi peaks distribution at the same time, thus can be used to the estimation of nonlinear and non-Gaussian problem effectively. Active Shape Models can represent every shape of the hand accurately. In this paper, we use particle filter to track the whole hand’s moving first, and then based on Active Shape Models to adjust each finger’s shape. This method can track each kind of the hand motion,which includes not only the whole hand’s translation, rotation and scale, but also each finger’s flexion and rotation.  We implement dynamic gesture recognition utilizing the result of the tracking part. We make improvements to the existed Hausdorff distance and make it suitable for the similarity measurement in noisy environment. At last, we realize the recognition of simple static hand gesture “scissors, rock and paper” in real scene based on the modified Huasdorff distance. The result is encouraging.
馆藏号XWLW1224
其他标识符200528014628071
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7447
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
陈小鹿. 复杂背景下手的检测、跟踪与识别[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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