Change Detection in Nonlinear Systems and Applications in Shape Analysis

非线性系统中的变化检测及其在形状分析中的应用

• 批准号: 0725849
• 负责人: Namrata Vaswani
• 金额: --
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0725849&HistoricalAwards=false
• 关键词: Change; Detection; Nonlinear; Systems; Applications

ECCS-0725849VaswaniChange detection is important in most tracking applications, since it is rarely true that the system model is truly time-invariant. Some examples include detecting motion model changes in target tracking or positioning applications; or detecting abnormal shape changes in computer vision/biomedical image analysis applications. In all of the above, the state is not directly observed. The observation is a noise-corrupted and nonlinear function of the state. The effectiveness of particle filters for such nonlinear/non-Gaussian tracking problems is already well known. Often, the changed system model is not known, i.e. the change or abnormality is not characterized. For example, the change may be a gradual one, for a constant velocity target slowly accelerating to a higher speed, or a sudden one. The approach is based on particle filter based algorithms for ``slow" and ?sudden?, unknown parameter, change detection. Robust design strategies will be developed and tested for realistic applications.Intellectual Merit: Most existing approaches can be classified as either based on adaptive filtering ideas or based on loss-of-track detection. Adaptive filtering approaches are either expensive or unreliable to implement. Loss-of-track based approaches detect abrupt changes almost immediately. However, slow changes, which result in a small loss of track per unit time, usually take a long time to get detected, or sometimes get missed. We propose a novel approach that utilizes the fact that slow changes get partially tracked, and uses this ``tracked part of the change" for detection using particle filters.Broader Impact: The developed algorithms will impact a large number of positioning, navigation or defense applications that require target tracking; video based surveillance applications that require abnormal behavior detection; biomedical signal/image sequence analysis applications where detected abnormalities can be indicators of disease, as well as many other applications in econometrics, finance, robotics and vision. Educational initiatives will include introduction of a graduate class on Adaptive filtering and Monte Carlo methods; modification of existing undergraduate classes; and senior design projects to implement and compare different shape extraction techniques for various applications.

ECCS-0725849Vaswani 变化检测在大多数跟踪应用中都很重要，因为系统模型很少是真正时不变的。一些示例包括检测目标跟踪或定位应用中的运动模型变化；或检测计算机视觉/生物医学图像分析应用中的异常形状变化。在以上所有情况中，状态都不是直接观察到的。观测结果是状态的噪声破坏和非线性函数。粒子滤波器对于此类非线性/非高斯跟踪问题的有效性已经众所周知。通常，改变的系统模型是未知的，即改变或异常没有被表征。例如，对于缓慢加速到更高速度的匀速目标，该变化可以是渐进的，也可以是突然的。该方法基于基于粒子滤波器的算法，用于“慢”和“突然”、未知参数、变化检测。将为实际应用开发和测试稳健的设计策略。智力优点：大多数现有方法可以根据以下分类：自适应滤波思想或基于跟踪丢失检测的自适应滤波方法要么成本昂贵，要么实施起来不可靠。然而，缓慢的变化会导致轻微的跟踪丢失。每单位时间，通常需要很长时间才能被检测到，或者有时会被遗漏。我们提出了一种新颖的方法，该方法利用缓慢变化被部分跟踪的事实，并使用这种“变化的跟踪部分”来使用粒子过滤器进行检测。影响：所开发的算法将影响大量需要目标跟踪的定位、导航或防御应用；需要异常行为检测的基于视频的监控应用；生物医学信号/图像序列分析应用，其中检测到的异常可以作为疾病的指标，以及计量经济学、金融、机器人和视觉领域的许多其他应用。教育举措将包括引入自适应过滤和蒙特卡罗方法的研究生课程；修改现有本科课程；和高级设计项目，以实施和比较不同应用的不同形状提取技术。