CAREER: Object Identification in Intelligent Systems

职业：智能系统中的对象识别

• 批准号: 9876181
• 负责人: Timothy Huang
• 金额: $ 20万
• 依托单位: Middlebury College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-05-01 至 2005-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9876181&HistoricalAwards=false
• 关键词: CAREER; Object; Identification; Intelligent; Systems

Object identification -- the task of deciding that two observed objects are in fact one and the same object -- is a fundamental requirement for any intelligent system or situated agent that reasons about individuals. An approach to this problem has been developed in recent years based on a Bayesian framework that determines the probability for an object's expected appearance at subsequent observations, given its current appearance. The theory has been successfully applied to the task of recognizing cars observed by cameras at widely separated sites in a freeway network. This research will build upon and extend theoretical results involving object identification and data association, and will show how to apply these results to real-world problems.This research has three primary thrusts. One aim is to integrate and generalize work from the data association community, which has focused on tracking independently moving objects whose state can be modeled by multivariate Gaussians, with work from the uncertainty in artificial intelligence community, which has produced a variety of compact state representations and associated inference algorithms. The goal is to present a unified framework with broader applicability than current theory. A second aim is to develop improved heuristic algorithms for approximating solutions to intractable problems that arise when the theory is applied to real-world domains. The goal is to develop new algorithms and demonstrate improved performance over existing ones. The third aim is to apply the theory to other real-world tasks, such as maintaining consistency and eliminating duplicate entries in databases. The goal is to illustrate the patterns of reasoning involved in moving from domain-independent theory to domain-specific problems. These research activities form part of a more general effort within the AI community to develop models and algorithms for reasoning in uncertain environments with noisy and imprecise sensors. The PI's education plans will strengthen the computer science program at Middlebury College in particular, and at liberal arts institutions in general, in two ways: through innovative, inter-disciplinary approaches to presenting introductory computer science concepts so as to increase the interest among first and second year college students in this field at a time when many of them might otherwise stop taking courses in mathematics or science; and through early involvement of students in undergraduate research projects. The project's ultimate goal is to provide a model for others to follow in ways to integrate research and education activities at undergraduate liberal arts institutions.

对象识别 - 确定两个观察到的对象实际上是一个且同一对象的任务 - 对于任何原因的智能系统或位置代理人都是基本要求。 近年来，基于贝叶斯框架，近年来已经开发了一种解决此问题的方法，该框架确定了对象在随后的观察结果中的预期外观的概率，鉴于其当前的外观。 该理论已成功地应用于识别摄像机在高速公路网络中广泛分离的站点上观察到的汽车的任务。这项研究将基于涉及对象识别和数据关联的理论结果并扩展理论结果，并将展示如何将这些结果应用于现实世界中的问题。这项研究具有三个主要推力。 一个目的是将数据协会社区的工作整合和概括，该社会的工作重点是跟踪独立移动的对象，其状态可以由多元高斯人建模，并与人工智能社区的不确定性相关的工作，该公司已经产生了各种紧凑的状态表示和相关的推理算法。 目的是提出一个比当前理论更广泛适用性的统一框架。 第二个目的是开发改进的启发式算法，以近似于将理论应用于现实世界领域时出现的棘手问题的解决方案。 目的是开发新算法并证明与现有算法相比，性能提高了。第三个目的是将理论应用于其他现实世界任务，例如保持一致性并消除数据库中的重复条目。 目的是说明从独立于领域的理论转变为特定于领域的问题所涉及的推理模式。这些研究活动构成了AI社区中更一般努力的一部分，以开发模型和算法，以在不确定的环境中进行嘈杂和不精确的传感器的推理。 PI的教育计划将尤其是米德尔伯里学院（Middlebury College），通常通过两种方式来增强米德尔伯里学院（Middlebury College）的计算机科学计划：通过具有创新的，跨学科的方法来提出入门计算机科学概念，以提高该领域的第一年和第二年大学生的兴趣，否则许多人可能停止在数学或科学方面的课程中停止参加课程； 以及通过学生早期参与本科研究项目。 该项目的最终目标是为其他人提供一个模型，以整合本科文科机构的研究和教育活动的方式。