CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleTracking and Detection of Small and Low-Contrast Infrared Objects
Thesis Advisor卢汉清
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弱小目标 跟踪与检测 因子采样 均值漂移 Gabor滤波器 Small And Low-contrast Object Tracking And Detection Factored Sampling Mean Shift Gabor Filters
Abstract视觉跟踪是计算机视觉领域中一个非常活跃的研究方向,红外弱小目标的 跟踪问题因为在军事上的广泛应用而受到关注。论文在借鉴已有计算机视觉算 法基础上,主要探讨了红外条件下运动目标的跟踪与检测问题,主要工作有: 1、利用目标灰度特征描述,结合因子采样滤波方法实现了跟踪红外场景 中的快速运动目标。当物体在杂乱背景中运动,视觉跟踪问题就变成了一个非 线性、非高斯的状态估计问题。因子采样作为一种基于模拟的方法,提供了估 计目标状态后验概率方便而有效的方法。我们将因子采样方法应用于跟踪红外 环境中的运动目标,取得了良好的效果。 2、把均值漂移(Mean Shift)优化方法用于解决跟踪问题是由D.Comaniciu et al[5,32]提出来的。我们在继承均值漂移优化方法的同时,增加目标灰度投影 特征描述,克服了由于核函数对称性所引起的目标描述不确定性的缺点。另外, 考虑到红外图像的特点,我们还设计了Gabor滤波器对运动目标进行图像增强。 3、红外目标的检测和目标再捕获对于红外跟踪系统也是相当重要的。我 们利用简单的背景相减法,实现运动目标的检测:并根据目标模板,在图像边 缘区域搜索与之相匹配的目标,实现对丢失目标的再捕获功能。在搜索匹配目 标的时候,我们采用高斯金字塔方法,从而减少了搜索范围,提高了运算速度。
Other AbstractVisual tracking is one of the top topics in computer vision. Tracking of small and low-contrast infrared objects is also an attractive research field for its abroad application in military. In this thesis, we focus on object tracking and detection in infrared environment on the basis of the visual object tracking algorithms. The contribution of the thesis are : 1. By using the feature of the grayscale of object and factored sampling algorithm, we achieved the tracking of the fast-moving object in infrared scenes. When an object is moving in cluttered environments, tracking becomes a problem of nonlinear and non-Gaussian state estimation. In this situation, as a simulation-based method, factored sampling provides a convenient and attractive approach of computing the state posterior distribution. Experiments demonstrate that it is a robust tracker with factored sampling. 2. D. Comaniciu et al[5,32] have used the Mean Shift optimization method in tracking of non-rigid objects. We expand upon the ideas of [5,32], exploiting the useful properties of the feature of the intensity projection because the central symmetry of the 2-dimensional kernel function can not characterize object as theonly one. In addition, Gabor filters are applied here to enhance the contrast of theobject with the background. 3. Objection detection and re-tracking is also an important part for the trackingsystems. We use a simple way, i.e. background subtraction to detect the movingobjects. About object re-tracking, by using the object template, we search the mostprobable location where the candidate object has the most likelihood with the objecttemplate. And the Gauss pyramid is used here to enhance the exhaustive searchalgorithm.
Other Identifier676
Document Type学位论文
Recommended Citation
GB/T 7714
陈辉. 红外弱小目标的跟踪和检测[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
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