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这项奖项是根据2009年《美国复苏与再投资法》（公法111-5）资助的。本提案中描述的项目旨在提高封闭环境中使用的计算机视觉算法的稳定性和鲁棒性。该研究努力研究了控制理论在增强闭环计算机视觉算法方面可能发挥作用。 理解控制与计算机视觉之间相互作用之间相互作用的概念是由于越来越多地寻求自动化过程的视觉。智能优点：作为传感器，可以通过实际的感应过程或通过成像的场景（例如，通过杂物，clutter，collet，collet，colled cold，可变性的假设等）来噪音进行成像系统。因此，在存在噪声和不确定性的情况下，视力任务可以解释为信号处理任务。通过为试图估算目标位置和目标边界的视觉跟踪系统的观察者，PI提议系统地得出一种高度稳定且可靠的视觉跟踪算法。研究努力涉及概率形状状态和相关测量模型的定义；用于动态对象和环境的完整状态观察者的推导；并使用机器学习对测量模型施加软几何约束。主要困难在于视野的丰富性以及所有视觉传感器固有的噪声，以及当目标本身既灵活又通过成像噪声而施加限制的脆弱性，而对测量模型进行了损害：拟议工作的应用：拟议的工作的应用将减少许多单身性，重复性，重复性的，超级计划的任务。 努力将由两个应用领域特别驱动：用于机场地面运营的安全性分析，建筑工地运营，地面挖掘操作以及微观蜂窝跟踪和分析。这两个应用领域都与民众的健康和福祉有关，有潜力挽救生命，并将产生积极的经济影响。