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基于判别式方法的目标跟踪和姿态估计算法研究
Alternative TitleObject Tracking and Pose Estimation Based on Discriminative Methods
张静
Subtype工学硕士
Thesis Advisor唐明
2009-06-02
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目标跟踪 多关节人体跟踪 支持向量机 图像流形 Object Tracking Articulated Body Tracking Svm Image Manifold
Abstract运动目标检测与跟踪是计算机视觉的主要研究方向之一,它在人机交互、智能监视、医学图像分析、移动机器人视觉导航、视频图像分析等领域中有着广泛的应用。本文在总结目前已有运动目标跟踪算法的基础上,提出了两种针对特定目标的视频跟踪算法。主要工作归纳如下: (1)针对一般物体的跟踪问题,提出了融合描述式和判别式方法的目标跟踪算法。该算法采用了多尺度的图像块表示,将目标跟踪问题转化为图像块的分类问题,通过结合判别式的二类支持向量机和描述式的在线一类支持向量机,有效地减轻了“模型飘移”问题对目标跟踪的影响。与目前最为相似且著名的实验比较证明了该算法的优越性和鲁棒性。(2)针对单摄像机系统下多关节人体姿态估计问题,实现了一种基于图像流形及目标运动状态连续性和约束性的交互式跟踪算法。与基于粒子滤波等产生式算法相比,该算法有效地利用各种运动假设约束,将跟踪问题转化为线性规划问题,对人体的简单运动,如走路等实现了目标运动状态的估计。
Other AbstractObject Tracking is an important computer vision problem and has been investigated during the past decades. It has wide application in human-computer interaction, intelligent surveillance, medical image analysis, robot navigation and video sequence analysis etc. In this dissertation, we focus on kernel tracking and silhouette tracking. Two new specific tracking algorithms are proposed for different applications. Our work can be concluded as follows: (1) For the general object tracking, a new hybrid kernel tracking algorithm is proposed, which combines discriminative and descriptive methods of object representation. In this method, object tracking is treated as classification problem of image pathes, multiple scale patches are used to model both object and background, the discriminative 2 class SVM and descripitive 1 class SVM corporate together to reduce the affect of “model shift” on tracking. The extensive experiment results show our algorithm’s superior to the state-of-the art algorithms. (2) For articulated pose estimation, an interactive tracking method based on image manifold and motion constrains is proposed. Compared with other generative methods, our algorithm take advantage of the combination of multiple motion constrains which greately reduce the computation complexity. The following experiment shows that our tracker can successfully estimate the pose state of human body.
shelfnumXWLW1413
Other Identifier200628014628067
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7500
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
张静. 基于判别式方法的目标跟踪和姿态估计算法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2009.
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