Geometric Particle Filters for Visual Tracking (Attn. Dr. Kishan Baheti)

用于视觉跟踪的几何粒子过滤器（Attn. Dr. Kishan Baheti）

• 批准号: 0625218
• 负责人: Allen Tannenbaum
• 金额: $ 24万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-15 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0625218&HistoricalAwards=false
• 关键词: Geometric; Particle; Filters; Visual; Tracking

B. PROJECT SUMMARYIn this research program, the PI proposes a novel combined geometric active contour/particlefiltering approach for tracking the boundaries of objects (i.e., planar shapes), when the observationis an image which may be a complicated nonlinear function of the closed curve. The advantageof using geometric active contours is that they allow topological changes (automatic merging andbreaking), and hence can be used to track multiple objects.More specifically, the particle filtering framework will be applied to the space of continuousclosed curves which is an infinite dimensional space. This is a particularly difficult problem sincegenerating Monte Carlo samples from a very large dimensional (theoretically infinite) system noisedistribution is computationally complex. Moreover, the number of samples required for accuratefiltering increases with the dimension of the system noise. The PI will show that as long as thenumber of dimensions of the system noise is small, even if the total state space dimension isvery large (or infinite), a particle filtering algorithm can be implemented which will allow himto develop practical robust tracking algorithms. In particular, the PI proposes to approximatecurve deformation using a time- varying finite dimensional representation. He will formulate theproblem as particle filtering with unknown static parameters and use a modification of a particlefilter that has been shown to be asymptotically stable for tracking static parameters.The main assumption is that even though the curve may be regarded as a point of an infinitedimensional space, "most of its deformation" for a given period of time can be approximated usinga small finite number of dimensions. But over time, this approximation may no longer sufficeand hence one must allow the number of dimensions and the finite dimensional basis to changewhenever the current approximation is unable to track with suffcient accuracy. For a numberof key scenarios, this assumption seems reasonable, and allows the use of infinite dimensionalobserver techniques for visual tracking.Intellectual Merit:The key objective of this project is the development of new methodologies for employing visualinformation in a feedback loop, the underlying problem of controlled active vision. This is achallenging problem both from the intellectual and practical points of view. Indeed, controlledactive vision, and in particular visual tracking requires the integration of techniques from controltheory, signal processing, and computer vision. This research program points the way to finding anew class of robust and hopefully real-time visual tracking schemes making use of all of the abovebuilding blocks.Broader Impact of Research Activity:The PI believes that the proposed synergy of vision, filtering, and control described in thisproposal may have a strong impact on tracking and active vision. Indeed, visual tracking providesa fundamental example of the need for controlled active vision. While tracking in the presence of adisturbance is a classical control problem, visual tracking raises new issues. Firstly, since camerasare part of the system, one must consider the nature of the disturbance from imaging sensors.Secondly, the feedback signal may require some interpretation of the image, e.g., segmentationof a target from its background, or an inference about an occluder. Finally, as visual processingbecomes more complex, the issue of processing time arises. Each of these problems must beanswered before target detection, and visually-mediated control can be provided for medical,commercial, or advanced weapon systems.

B. Project Summaryin本研究计划，PI提出了一种新型的组合几何活性轮廓/粒子滤光方法，用于跟踪对象的边界（即平面形状），当观察者观察时，图像是一个可能是封闭曲线的复杂非线性函数的图像。使用几何活性轮廓的优势是，它们允许拓扑更改（自动合并和破坏），因此可以用于跟踪多个对象。更具体地说，粒子过滤框架将应用于连续曲线的空间，这是一个无限的尺寸空间。这是一个特别困难的问题，因为从非常大的尺寸（理论上无限）系统的噪声分布在计算上是复杂的，这是一个特别困难的问题。此外，准确滤光所需的样本数量随系统噪声的尺寸增加。 PI将表明，只要系统噪声的尺寸的尾声很小，即使总状态空间维度为大（或无限），就可以实现粒子过滤算法，这将使他能够开发实用的强大跟踪跟踪算法。特别是，PI使用时间变化的有限尺寸表示提出了近似值变形。他将用未知的静态参数提出问题作为粒子过滤，并使用粒子滤光器的修改，该粒子被证明在跟踪静态参数上是渐近稳定的。主要假设是，即使曲线可以被视为无限量化空间的点，“对于给定时间段的大多数限制了尺寸，都可以限制时间的数量。但是随着时间的流逝，这种近似值可能不再足够，因此必须允许尺寸的数量和有限的尺寸基础，以改变当前近似值无法以足够的精度跟踪。对于关键方案的数字，此假设似乎是合理的，并且允许使用无限的尺寸操作器技术进行视觉跟踪。IntlectualFure：该项目的关键目的是开发用于在反馈循环中采用可视化信息的新方法，这是对受控活动的潜在活跃视觉的潜在问题。从智力和实践的角度来看，这都是令人难以置信的问题。实际上，受控视觉，尤其是视觉跟踪需要从控制理论，信号处理和计算机视觉中整合技术。该研究计划指出了寻找新的强大和希望实时的视觉跟踪方案的方法，利用所有上述建筑块。研究活动的影响力的影响：PI认为，在此范围中所描述的拟议协同作用可能对跟踪和积极的视觉产生强大的影响。实际上，视觉跟踪提供了对受控主动视觉需求的基本示例。尽管在存在阻碍的情况下跟踪是一个经典的控制问题，但视觉跟踪会引发新问题。首先，由于系统的摄像头部分，必须考虑成像传感器的干扰性质。第二，反馈信号可能需要对图像进行某种解释，例如，从其背景中分割目标或对封锁者的推断。最后，随着Visual ProcessingBecomes更加复杂，会出现处理时间的问题。这些问题中的每一个都必须在目标检测前进行豆腐，并且可以为医疗，商业或先进的武器系统提供视觉介导的控制。