RI: Small: Modeling and Learning Visual Similarities Under Adverse Visual Conditions

RI：小：在不利视觉条件下建模和学习视觉相似性

• 批准号: 1619078
• 负责人: Ying Wu
• 金额: $ 44万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619078&HistoricalAwards=false
• 关键词: RI; Small; Modeling; Learning; Visual

In many emerging applications such as autonomous/assisted driving, intelligent video surveillance, and rescue robots, the performances of visual sensing and analytics are largely jeopardized by various adverse visual conditions in complex unconstrained environments, e.g., bad weather and illumination conditions. This project studies how and to what extend such adverse visual conditions can be coped with. It will advance and enrich the fundamental research of computer vision, and bring significant impact on developing "all-weather"computer vision systems that benefit security/safety, autonomous driving, and robotics. The project contributes to education through curriculum development, student training, and knowledge dissemination. It also includes interactions with K-12 students for participation and research opportunities. This research seeks innovative solution to overcome adverse visual conditions for visual sensing and analytics. It explores a unified approach that avoids explicit image restoration that is in general computationally demanding. It is focused on learning the "alignment" between the two image spaces under adverse and normal conditions, rather than learn everything from scratch. Acting on low-quality data directly without image restoration, this research leads to innovative and computationally efficient solutions to handle adverse visual conditions. Visual restoration can also be done as by-products, and the same approach also provides a general solution to target attribute estimation. The research is focused on: (1) constructing a principled model, called space alignment that models and learns visual similarity, and its theoretical foundation, (2) developing new effective visual matching and tracking approaches based on learning the appropriate visual similarity under various adverse visual condition, (3) investigating visual attribute estimation and identification via learning reconstruction-based visual regression, and (4) developing effective and efficient tools and prototype systems for visual detection, identification, tracking and recognition.

在自动/辅助驾驶、智能视频监控和救援机器人等许多新兴应用中，视觉传感和分析的性能在很大程度上受到复杂无约束环境（如恶劣天气和照明条件）中各种不利视觉条件的影响。该项目研究如何以及在多大程度上应对这种不利的视觉条件。它将推进和丰富计算机视觉的基础研究，并对开发有益于安防/安全、自动驾驶和机器人的“全天候”计算机视觉系统产生重大影响。该项目通过课程开发、学生培训和知识传播来促进教育。它还包括与K-12学生的互动，以获得参与和研究机会。本研究寻求创新的解决方案，以克服视觉传感和分析的不利视觉条件。它探索了一种统一的方法，避免了通常需要计算的显式图像恢复。它侧重于在不利和正常条件下学习两个图像空间之间的“对齐”，而不是从头开始学习一切。在没有图像恢复的情况下直接对低质量数据进行处理，这项研究导致了创新和计算效率的解决方案来处理不利的视觉条件。视觉恢复也可以作为副产品来完成，同样的方法也为目标属性估计提供了一个通用的解决方案。研究的重点是：(1)构建建模和学习视觉相似性的空间对齐原则模型及其理论基础；(2)基于学习各种不利视觉条件下的适当视觉相似性，开发新的有效的视觉匹配和跟踪方法；(3)研究基于学习重建的视觉回归的视觉属性估计和识别方法。(4)开发用于视觉检测、识别、跟踪和识别的有效工具和原型系统。