Automatic Active Vision Based Target Tracking for Recognition in Underwater

9711528基于Negahdariour视觉的自动目标跟踪识别是智能或自主机器人系统的一项重要能力。应用包括搜索(丢失的)物体，定位附近的障碍物，测绘，与建筑物或移动物体对接监视，以及测量。尽管基于视觉的地面和机载ATR系统的发展取得了进展，但由于海底环境的独特条件，实现类似能力的潜水器系统是一个具有挑战性的问题。这项研究工作涉及到基于视觉的ATR问题的研究，使用基于理论的科学方法，依赖于基于物理的建模和主动传感范式。其目标是提供一种水下机器人系统，具有自主执行以下功能的智能：探索邻近环境，而不与附近的障碍物相撞。找到感兴趣的对象，并专注于它们。做出明智的举动并执行适当的行动，以有效地检查目标并获得有用的信息以进行识别。通过集成来自多个视图和不同成像条件的信息来构建关于场景的知识库。具体地说，研究人员寻求开发用于从视频数据中获得关于场景中的对象的信息(例如，三维形状、范围、运动、反射特性)的技术，根据：考虑场景几何和反射特性、照明和观察几何以及海洋光学特性的水下图像形成的基于物理的模型。新颖的理论，用于自适应在线调整传感器参数(例如，视场、关注焦点)以及观察和场景照明的位置和方向，以优化数据质量和信息内容。对从不同视角和不同成像条件下获得的视觉信息进行配准和合并的数学方法。***

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. ***