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基于轮廓模型的概率跟踪研究
Alternative TitleResearch on Contour Model_based Probabilistic Tracking
余旭文
Subtype工学硕士
Thesis Advisor卢汉清
2002-05-01
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跟踪 Condensation算法 形状空间 遮挡处理 Tracking Condensation Algorithm Shape Space Occlusion Reasoning
Abstract跟踪算法作为动态图像分析重要而关键的一步,一直是视觉中的一个重要课题。 无论是在传统的军事领域还是在很多新的民用、商用系统中,跟踪其有广泛的应用前 景。对于各种复杂情况跟踪,如杂乱背景、部分和完全遮挡等等,虽然已经提出了很 多解决方案,但仍然有许多问题需要进一步的研究。 本文的主要工作是采用形状空间的方法实现了基于Condensation的轮廓跟踪, 并对其中的一些理论性的问题作了有益的探讨和实现。Condensation方法作为近年 提出的有效的跟踪方法,虽然算法已经比较成熟,但是依旧是一个开放的体系,可以 有很多种改进和实现。在一般干扰的情况下,我们改进了系统的观测模型,不仅强调 轮廓的模型不变性,而且强调系统的抗干扰性能,实验表明观测模型的改进经过概率 传播的强化,在比较复杂的背景条件下、在跟踪目标发生一定的变化(如人体姿态的 变化)、及部分遮挡的条件下都获得的较好的跟踪效果。 基于以上的工作,在系统本身已经具有较好的鲁棒性的前提下,我们进一步处理 了较长时间的完全遮挡这一强干扰问题,利用了基于形状空间参数的判断处理过程, 在采用了一些高层假设的前提下,提出了一种初步解决的方案。
Other AbstractObject tracking, as an important step in the Dynamic Image Analysis, is one important topic of Computer Vision., It has important and potential applications in the area of military affair and many new civil and commercial systems. There are many object tracking algorithms under complicated conditions, such as in dense visual clutter , partial occlusion or complete occlusion , but it is necessary to do further research. In this thesis, we improved the contour tracking based on Condensation algorithm with Shape Space Representation and do some further :research on this algorithm. Condensation algorithm is efficient, however, we can improve it from many aspects. The contributions of this thesis include: 1) We improve the Observation Model of the tracking system.In this model, we emphasis not only the invariability but the anti-disturbance ability of the model. Experiment results showed that it con obtain good tracking results even if the object is in dense visual clutter, partial occlusion. 2) Based on the work above, we deal with the complete occlusion, which Js a strong disturbance. Because the tracking system is robust, we take some hypothesis and use the shape space parameters to verdict, and at last we give a method to solve this problem.
shelfnumXWLW629
Other Identifier629
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6867
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
余旭文. 基于轮廓模型的概率跟踪研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2002.
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