CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleModel-Based Tracking of Walking People
Thesis Advisor谭铁牛
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基于模型的行人跟踪 人体模型 运动模型 运动约束 基于动力学的跟踪 粒子滤波 动态模型 Modcl-bascd Tracking Of Walking People Human Body Model Motion Model Motion Constraints Kinematics-based Tracking Particle Filte
Abstract人运动的视觉分析就是通过对图像或视频进行处理,获取人体姿态和运动参数,进行姿态识别、语义分析及行为理解,它在虚拟现实、智能监控、人机交互、运动分析、基于模型的编码等方面具有广泛的应用前景和潜在的经济价值。近年来,人运动的视觉分析受到国内外学术界和企业界的广泛关注,但作为计算机视觉领域中的一个热点和难点,仍然有很多理论与技术问题需待解决。本文围绕人运动的视觉分析中的重要课题——基于模型的行人跟踪——展开研究,它不仅涉及底层视觉的许多问题,还是高层视觉处理的基础。基于模型的跟踪是解决行人跟踪问题的一般性框架。我们以这个框架为基础,在模型知识的学习和表示、姿态评价函数、初始化和搜索策略等子课题做了细致的分析和探讨,提出了一些新的算法,并获得了一些有价值的实验结论。 (1)从大量训练数据学习得到高斯表示的运动模型,它形式紧凑,在预测和初始化方面有着重要的作用。我们也细致地分析了人体的运动约束,用高斯 混合模型模拟关节角度的分布,通过求解置信区间得到关节的活动范围。相邻关节的依赖性用条件概率建模,从训练数据中学习得到概率分布的参数。 (2)提出一种比较可靠的运动检测方法,用来提取图像的边缘特征和区域 特征。然后,这两种特征被同时考虑到姿态评价函数之中,一方面利用边缘的 精确定位提高评价的准确性,另一方面利用区域的丰富信息提高鲁棒性。 (3)行人跟踪是一个高维优化问题,本文采用层次化的搜索策略将其分解 为全局位置估计和关节角度优化。对于后者,本文以弹力模型为基础,根据刚体绕定轴的转动定律,提出了基于动力学的算法,递归地优化关节角度。 (4)为了避免动力学方法的不足,本文进一步将基于模型的行人跟踪纳入 概率框架之下,使用粒子滤波进行优化。根据粒子滤波理论,本文着重分析初 始化和动态模型。我们用前Ⅳ帧的时空信息和模型知识初始化人体姿态,融合跟踪的历史信息、运动模型以及运动约束来设计动态模型。
Other AbstractVisual analysis of human motion is currently one of the most active research topics in computer vision. It aims to recover body poses and motion parameters from static images or video sequences. The recovered data, used for pose recognition, semantic analysis and behavior understanding, have a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, advanced perceptual interfaces, motion analysis, and model-based coding. In recent years, although visual analysis of human motion received increasing attention from both academia and industry, many theoretical and technical problems remain open. This thesis focuses on an important subject in this field, i.e., model-based tracking of walking people, which not only involves many issues of low-level vision but also provides motion data for high-level visual analysis. Model-based tracking of walking people is a general framework for people tracking. Under such a framework, we analyze the important modules (including learning and representation of prior knowledge, pose evaluation function, initialization, search strategy, and so on), describe some novel algorithms, and draw some useful conclusions. Our contributions are summarized as follows. (1) A compact motion model is learnt from a volume of training examples. The model, represented as Gaussian distributions, plays an important role in prediction and initialization. We also carefully analyze the human motion constraints: intervals of joint angles and dependencies of neighboring joints. The former are derived from confidential intervals of joint angle distributions that are modeled as mixture Gaussians. The latter are represented by conditional distributions whose parameters are learnt from training data. (2) We propose a robust approach to motion detection that is applied to extraction of features of edges and region information. Then both features are combined into the pose evaluation function to obtain accuracy and robustness. (3) People tracking is an optimization problem of high dimensionality. We decompose it into two sub-problems: estimation of global position and refinement of joint angles. As to the latter, we propose an effective approach to recursively refine each joint separately. This approach is based on the spring model and rotation kinematical equation. (4) To avoid the deficiencies of the above approach, we also track people in a probabilistic framework using a particle filtering. According to particle filtering, we emphasize on the initialization and dynamic model. We use the spatio-temporal information of the first N frames and prior knowledge of human motion to initialize the body pose. Then tracking history, motion model and motion constraints are fused to design our dynamic model.
Other Identifier691
Document Type学位论文
Recommended Citation
GB/T 7714
宁华中. 基于模型的行人跟踪[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[宁华中]'s Articles
Baidu academic
Similar articles in Baidu academic
[宁华中]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[宁华中]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.