Automatic Active Vision Based Target Tracking for Recognition in Underwater

基于自动主动视觉的水下目标跟踪识别

• 批准号: 9711528
• 负责人: Shahriar Negahdaripour
• 金额: $ 24.61万
• 依托单位: University of Miami
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-01-15 至 2002-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9711528&HistoricalAwards=false
• 关键词: Automatic; Active; Vision; Based; Target

9711528 Negahdaripour Vision-based automatic target tracking for recognition (ATR) is an important capability for intelligent or autonomous robotics systems. Applications include searching for (lost) objects, localization of nearby obstacles, mapping, docking with structures or moving objects surveillance, and surveying. Despite progress on the development of vision- based terrestrial and airborne ATR systems, realization of similar capabilities for submersible systems is a challenging problem due to the unique conditions of the undersea environment. This research effort involves a study of the vision-based ATR problem using a theory-based scientific approach, resting on physics-based modeling and active sensing paradigms. The goal is to provide an underwater vehicle system with the intelligence to perform the following functions autonomously: Explore immediate surroundings without collision with nearby obstacles. Locate objects of interest and fixate on them. Make intelligent moves and execute appropriate actions to efficiently examine the target and obtain useful information for recognition. Construct a knowledge base about the scene by integration of information from multiple views and different imaging conditions. Specifically, the researchers seek to develop techniques for obtaining information (e.g., three-dimensional shape, range, motion, reflectance properties) about objects in the scene from video data, according to: Physics-based models of underwater image formation, taking into account scene geometry and reflectance characteristics, illumination and viewing geometry, and ocean optical properties. Novel theories for adaptive online adjustment of sensor parameters (e.g., field of view, focus of attention), and positions and directions of viewing and scene illumination, to optimize data quality and information content. Mathema tical approach to registration and merging of visual information, obtained from various views and under different imaging conditions. ***

9711528 Negahdaripour基于视觉的自动目标跟踪识别（ATR）是智能或自主机器人系统的重要功能。 应用包括搜索（丢失）物体，定位附近的障碍物，测绘，与结构对接或移动物体监视和测量。 尽管基于视觉的陆地和机载ATR系统的开发取得了进展，但由于海底环境的独特条件，实现潜水系统的类似能力是一个具有挑战性的问题。 这项研究工作涉及基于视觉的ATR问题的研究，使用基于理论的科学方法，基于物理建模和主动传感范例。 其目标是提供一种具有智能的水下航行器系统，以自主执行以下功能： 探索周围的环境，而不会与 附近的障碍。 找到感兴趣的对象并注视它们。 做出明智的举动并执行适当的行动 为了有效地检查目标并获得有用的 信息识别。 通过以下方式构建关于场景的知识库： 整合来自多个视图和不同 成像条件。 具体来说，研究人员寻求开发获取信息的技术（例如，三维形状、范围、运动、反射特性），根据： 水下成像的物理模型， 考虑到场景几何和反射 特性、照明和观察几何结构，以及 海洋光学特性 传感器自适应在线调整的新理论 参数（例如，视野，注意力焦点），以及 观察和场景照明的位置和方向， 优化数据质量和信息内容。 图像配准与合并的数学方法 视觉信息，从各种视图和下获得 不同的成像条件。 ***