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Alternative TitleTracking Articulated Human Motion in Monocular Action Sequences
Thesis Advisor唐明
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword人体模型 人体姿态估计 人体运动跟踪 Human Model Human Pose Detection Human Motion Tracking
Abstract近年来,人体运动的跟踪与分析在图像处理与计算机视觉领域引起许多学者的关注。这一课题在智能监视系统、虚拟现实、高级用户接口、运动分析和基于模型的图像编码等方面具有广阔的应用前景。利用图像序列进行人体运动的跟踪与分析包含三个基本内容:1)从复杂背景中提取运动人体;2)人体运动的跟踪和标定;3)人体行为的识别和理解。其中,人体运动的跟踪和标定是人体运动跟踪与分析过程的关键,是进一步识别和理解人体运动行为的基础,人体行为的识别和理解达到了人体运动分析的最高境界,但目前还远达不到这一点,因此研究工作主要集中在前两项上。本文提出了基于单目视频的人体运动跟踪方法,该方法的处理流程主要两个部分:首先,对于给定的视频,抽取每帧图像中的特征并检测人体姿态;然后,根据在视频中人体运动轨迹的平滑性,迭代式得估计整个图像序列中的人体运动。 本文针对序列图像中人体运动跟踪问题本身的特点进行了深入的研究,涉及到了一些有关图像处理、模式识别、函数优化的基本问题。在本文中,主要的工作和贡献有:  该算法不需要先验运动图片库进行模型学习过程,因而对先验知识没有要求,所以可以使用于一般生活场景中。  算法针对树状人体模型在单目视觉的姿态估计中的固有问题,修改了传统的维特比算法,通过加入K-最优姿态寻找,巧妙得加入人体模型中的全局性约束,从而在每帧图像中得到合理的姿态解。  通过对整个运动序列做姿态歧义分析和遮挡推断,可以克服视频跟踪的时候所遇到的诸如运动模糊、自遮挡等问题, 从而最终得到整个视频中的人体运动。 总的说来,本文针对当前人体运动重建领域主流方向之一的单目视觉的人体运动跟踪作了有益的探索。由于单目视频所提供的有限的信息,使得所研究的问题有着更大的难度和更艰巨的挑战,但也使本文的研究具有独特的意义和广泛的应用前景。
Other AbstractHuman motion tracking has been received increasing attention from researchers in the fields of image processing and computer vision during the past few years. It has a lot of applications in video edit, virtual reality, advanced user interface and human motion analysis, etc. The procedure of the articulated human motion tracking and analysis in monocular action image sequences involves three main stages: (1) human body segmentation in a complex scene; (2) human motion tracking and body structure re-construction; (3) motion analysis and action recognition. As the base of the human action recognition and understanding, human motion tracking and body structure re-construction is the key of the whole procedure. Action recognition is the highest level of motion analysis, but now it is far from application. The researching work mainly focuses on the first stage and second stage. This paper proposes a novel method of tracking human motion from a single view. The correspondence between the human region of the image and the human body model is established through analyzing the feature extracted from the image sequences and estimating the human body pose, then optimizing the whole state sequences in the form of motion trajectory, which makes the MAP (Maximum A Posterior) solution of the whole state sequence of the pose efficiently obtained in the recursively pose updating process, and ultimately recovering the sequence of human motion. In this thesis, we study human pose tracking from monocular image sequences which involves some basic problems in image processing, pattern recognition and energy function optimization, etc. The main contributions of this thesis include the following issues:  No learning process of human model prior from image database is needed, and no limits on the video scene are required.  By integrating a post-process called K-Best Viterbi Search into the conventional Viterbi, the global constraints are elegantly added to get reasonable results while the time complexity of the inference keeps quite low.  The pose ambiguity is carefully analyzed, and the missing data caused by occlusion is connected by the trajectory smoothing. In a word, in this thesis, we have made fruitful attempts and satisfactory progress on tracking articulated human motion in monocular action sequences.
Other Identifier200428014628031
Document Type学位论文
Recommended Citation
GB/T 7714
吴倩. 单目视频中的人体运动跟踪[D]. 中国科学院自动化研究所. 中国科学院研究生院,2007.
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