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

在许多新兴应用中，例如自主/辅助驾驶、智能视频监控和救援机器人，视觉感测和分析的性能在很大程度上受到复杂无约束环境中的各种不利视觉条件的危害，例如，恶劣的天气和照明条件。本项目研究如何以及在何种程度上可以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，并对开发有利于安全/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。该项目通过课程编制、学生培训和知识传播促进教育。它还包括与K-12学生的参与和研究机会的互动。 这项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，避免了显式的图像恢复，在一般的计算要求。它专注于学习在不利和正常条件下两个图像空间之间的“对齐”，而不是从头开始学习一切。直接对低质量数据采取行动而不进行图像恢复，这项研究导致了创新和计算效率高的解决方案，以处理不利的视觉条件。视觉恢复也可以作为副产品来完成，并且相同的方法也提供了目标属性估计的一般解决方案。研究的重点是：（1）建立了一个描述和学习视觉相似性的原则性模型，称为空间对齐，及其理论基础，（2）在各种不利视觉条件下，基于学习适当的视觉相似性，开发新的有效的视觉匹配和跟踪方法，（3）通过学习基于重建的视觉回归，研究视觉属性估计和识别，以及（4）开发有效和高效的视觉检测、识别、跟踪和识别工具和原型系统。

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.