RI:Small:Collaborative Research: Understanding Human-Object Interactions from First-person and Third-person Videos

无处不在的摄像头，加上不断增加的计算资源，正在极大地改变着视觉数据及其分析的本质。城市正在采用网络摄像头系统进行警务和智能资源分配，个人正在使用可穿戴设备记录他们的生活。为了让这些摄像头系统变得真正智能和有用，它们必须理解场景中有趣的物体并检测正在进行的活动/事件，同时共同考虑来自多个来源的连续24/7视频。医院、养老院和公共场所的这种对象级和活动级的意识将为残疾人和老年人提供辅助和生活质量技术，提供智能监控系统以防止犯罪，并允许明智地利用环境资源。该项目将研究结合第一人称视频（来自可穿戴摄像机）和第三人称视频（来自静态环境摄像机）的新型计算机视觉算法，以联合识别人类、物体及其相互作用。其关键思想是结合两种视角的互补和独特优势，进行联合视觉场景理解。为此，它将创建一个新的数据集，并开发新的算法，学习跨视图共同识别对象，通过两个视图学习人-对象和人与人之间的关系，并对视频进行匿名化以保护用户的隐私。该项目将提供新的算法，这些算法有可能有利于智能环境、安全和生活质量辅助技术的应用。该项目还将开展补充教育和推广活动，吸引学生参与研究和STEM。该项目将开发新的算法，从联合第一人称视频（来自可穿戴摄像机）和第三人称视频（来自静态环境摄像机）中学习，以联合识别人类、物体及其相互作用。第一人称视图是理想的对象识别，而第三人称视图是理想的人类活动识别。因此，本项目将研究具有挑战性的问题的独特解决方案，否则在单独分析每个观点时将难以克服这些问题。主要的研究方向将是：(1)创建基准的第一人称和第三人称视频数据集来研究这一新问题；并开发算法(2)学习在两种视图之间建立对象和人的对应关系；(3)学习跨视图的对象-动作关系；(4)对视觉数据进行匿名化处理，实现保护隐私的视觉识别。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Ubiquitous cameras, together with ever increasing computing resources, are dramatically changing the nature of visual data and their analysis. Cities are adopting networked camera systems for policing and intelligent resource allocation, and individuals are recording their lives using wearable devices. For these camera systems to become truly smart and useful for people, it is crucial that they understand interesting objects in the scene and detect ongoing activities/events, while jointly considering continuous 24/7 videos from multiple sources. Such object-level and activity-level awareness in hospitals, elderly homes, and public places would provide assistive and quality-of-life technology for disabled and elderly people, provide intelligent surveillance systems to prevent crimes, and allow smart usage of environmental resources. This project will investigate novel computer vision algorithms that combine 1st-person videos (from wearable cameras) and 3rd-person videos (from static environmental cameras) for joint recognition of humans, objects, and their interactions. The key idea is to combine the two views' complementary and unique advantages for joint visual scene understanding. To this end, it will create a new dataset, and develop new algorithms that learn to recognize objects jointly across the views, learn human-object and human-human relationships through the two views, and anonymize the videos to preserve users' privacies. The project will provide new algorithms that have the potential to benefit applications in smart environments, security, and quality-of-life assistive technologies. The project will also perform complementary educational and outreach activities that engage students in research and STEM.This project will develop novel algorithms that learn from joint 1st-person videos (from wearable cameras) and 3rd-person videos (from static environmental cameras) for joint recognition of humans, objects, and their interactions. The 1st-person view is ideal for object recognition, while the 3rd-person view is ideal for human activity recognition. Thus, this project will investigate unique solutions to challenging problems that would otherwise be difficult to overcome when analyzing each viewpoint in isolation. The main research directions will be: (1) creating a benchmark 1st-person and 3rd-person video dataset to investigate this new problem; and developing algorithms that (2) learn to establish object and human correspondences between the two views; (3) learn object-action relationships across the views; and (4) anonymize the visual data for privacy-preserving visual recognition.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.