Systems Theoretic Methods for Dynamic Problems in Computer Vision

动态视觉具有独特的优势，可以以具有成本效益的方式提高大部分人口的生活质量。 现场分析功能可以预防犯罪，使老年人能够继续独立生活，并监测和协调对环境威胁的反应，以尽量减少其影响。然而，限制视觉技术广泛使用的一个关键因素是它们潜在的脆弱性。该项目旨在消除这一限制。研究团队正在开发一种系统化的方法来实现强大的动态视觉，该方法在一个通用框架中解决了几个关键的子问题-跟踪，外观建模，运动结构和基于运动的分割。它的概念骨干是一个统一的，运营商理论的方法，强调使用动态模型来解决鲁棒性和计算复杂性的问题。所提出的框架的优点包括以下能力：（a）重铸到一个凸优化形式服从真实的时间实现的问题范围广泛。(b)提供最坏情况下的界限和保证性能的证书，帮助减少在线计算负担时，解决这些问题。（c）利用摄像机合作优化性能。(d)利用目标的其他可用信息来提高鲁棒性，并在当前模型不再有效时（例如由于数据过时）伪造当前模型。通过使用计算机视觉，在本科和研究生课程中传达关于鲁棒性和计算复杂性的想法，教育积极融入该项目。这项研究工作的结果，包括演示的视频剪辑，定期发布在鲁棒系统实验室（http：robustsystems.ece.neu.edu）网站上。

Dynamic vision is uniquely positioned to enhance the quality of life for large segments of the population in a cost effective way. Scene analysis capabilities can prevent crime, allow elderly people to continue living independently and monitor and coordinate responses to environmental threats to minimize their effect. However, a critical factor limiting widespread use of vision techniques is their potential fragility. This project aims precisely at removing this limitation.The research team is developing a systematic approach to robust dynamic vision that addresses several key sub-problems - tracking, appearance modeling, structure from motion, and motion-based segmentation -- in a common framework. Its conceptual backbone is a unified, operator--theoretic approach stressing the use of dynamic models to address robustness and computational complexity issues. Advantages of the proposed framework include the abilities to:(a) Recast a wide range of problems into a convex optimization form amenable to real time implementations. (b) Furnish worst--case bounds and guaranteed performance certificates that help reducing the on--line computational burden when solving these problems. (c)Exploit camera cooperation to optimize performance.(d) Take advantage of additional information available about the target to improve robustness and falsify the current models when no longer valid, for instance due to data obsolescence.Education is proactively integrated into this project by using computer vision to convey ideas on robustness and computation complexity in undergraduate and graduate courses. Results of this research effort, including video clips with demos are regularly posted at the Robust Systems Lab (http://robustsystems.ece.neu.edu) website.)