CAREER: Observer Design for Intelligent Visual Tracking

职业：智能视觉跟踪的观察者设计

• 批准号: 0846750
• 负责人: Patricio Vela
• 金额: $ 40万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0846750&HistoricalAwards=false
• 关键词: CAREER; Observer; Design; Intelligent; Visual

Proposal Number: 0846750This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The project described in this proposal seeks to improve the stability and robustness of computer vision algorithms used in closed-loop systems. The research Endeavour examines the role control theory may play in enhancing closed-loop computer vision algorithms. The concept of understanding the interplay between control and computer vision is achieving prominence as vision is increasingly sought for automating processes.Intellectual Merit: As a sensor, the imaging system can be wrought with noise, either through the actual sensing process or through the geometry of the imaged scene (e.g., through clutter, occlusions, variable hypotheses, etc.). Thus, the vision task can be interpreted as a signal processing task in the presence of noise and uncertainty. By deriving observers for visual tracking systems that seek to estimate both target location and target boundary, the PI proposes to systematically derive a highly stable and robust visual tracking algorithm. The research Endeavour involves the definition of a probabilistic shape state and associated measurement models; the derivation of a full state observer for dynamic objects and environments; and the use of machine learning to impose soft and hard geometric constraints on the measurement model. The main difficulties lie in the richness of the visual field coupled with the noise inherent to all visual sensors, and the fragility of imposing constraints on the measurement model when the target itself is both flexible and corrupted by imaging noise.Broader Impact:Application of the proposed work will reduce the amount of human labor involved in many monotonous, repetitive, or time-intensive tasks. Efforts will be specifically driven by two application areas: workforce tracking for safety analysis of airport ground operations, construction site operations, surface mining operations, and microscopic cellular tracking and analysis. Both of these application areas are related to the health and well-being of the populace, have the potential to save lives, and will have a positive economic impact.

提案编号：0846750该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。本提案中描述的项目旨在提高闭环系统中使用的计算机视觉算法的稳定性和鲁棒性。Endeavour研究了控制理论在增强闭环计算机视觉算法中可能发挥的作用。 理解控制和计算机视觉之间的相互作用的概念是实现突出的视觉越来越多地寻求自动化过程。智力优点：作为一个传感器，成像系统可以与噪声，通过实际的传感过程或通过成像场景的几何形状（例如，通过杂乱、遮挡、可变假设等）。因此，视觉任务可以被解释为存在噪声和不确定性的信号处理任务。通过推导视觉跟踪系统的观测器，寻求估计目标位置和目标边界，PI建议系统地推导出一个高度稳定和鲁棒的视觉跟踪算法。研究奋进涉及概率形状状态和相关测量模型的定义;推导动态对象和环境的全状态观测器;以及使用机器学习对测量模型施加软硬几何约束。主要的困难在于丰富的视野加上固有的所有视觉传感器的噪声，和脆弱性的测量模型施加约束时，目标本身是灵活的和损坏的成像noise.Broader影响：应用所提出的工作将减少涉及许多单调，重复性，或时间密集型任务的人力。 工作将具体由两个应用领域驱动：机场地面作业、建筑工地作业、露天采矿作业安全分析的劳动力跟踪，以及微观细胞跟踪和分析。这两个应用领域都与民众的健康和福祉有关，有可能挽救生命，并将产生积极的经济影响。