CAREER: Visual Analysis of High-Dimensional Motion: A Distributed/Collaborative Approach

这个项目是关于从视频中分析高维运动（HDM）。HDM是指人体的各种高自由度复杂运动，包括人体的关节运动、弹性物体的变形以及多个遮挡目标的多重运动。这个项目的目标是克服嵌入在这个具有挑战性的视觉推理问题的维数灾难，通过系统地追求一个新的分布式/协作的方法，统一各种HDM。与集中式方法有很大不同，新方法基于马尔可夫网络模型将HDM分布到子部件运动的网络表示中。然后，禁止HDM推理任务可以有效地和高效地完成的“协作”之间的分布式，但相互约束的小规模视觉推理过程，所揭示的提出的理论研究，该模型和所提出的协同粒子网络算法实现。这种新方法预计将大大提高效率，更具可扩展性和灵活性，并且更加强大。本项目通过使快速准确的人体跟踪和检测技术成为可能，对智能视频监控产生影响，并对人机交互和医学成像的研究产生重大影响。本研究与旨在促进学习和创新的教育活动相联系，通过（1）开发视觉计算和统计建模的综合课程;（2）通过创新的课程项目和现实世界的应用，激励学生探索未知的前沿;（3）通过会议和网站，与其他相关的研究团体联系;（4）通过创建Vision OpenHouse活动，向公众、女性和少数民族学生传播研究成果。

This project is about analyzing high-dimensional motion (HDM) from video. HDM refers to various complex motions with high degrees of freedom, including the articulation of human body, the deformation of elastic shapes and the multi-motion of multiple occluding targets. The goal of this project is to overcome the curse of dimensionality embedded in this challenging visual inference problem, by systematically pursuing a new distributed/collaborative approach that unifies various HDMs. Substantially different from centralized methods, the new approach distributes HDM into a networked representation of subpart motions, based on Markov network models. Then the prohibitive HDM inference tasks can be effectively and efficiently fulfilled by the "collaborations" among the distributed but mutually constrained small-scale visual inference processes, as revealed by the proposed theoretical study of this model and implemented by the proposed collaborative particle network algorithms. This new approach is expected to be significantly more efficient, more scalable and flexible, and more robust. This project has impact on intelligent video surveillance by making possible fast and accurate human tracking and detection techniques, and significantly benefits the research of human-computer interaction and medical imaging.The research is linked to educational activities aiming at the promotion of learning and innovation through (1) developing an integrated curriculum for visual computing and statistical modeling; (2) motivating students to explore the unknown frontiers via innovative course projects and real-world applications; (3) outreaching to other related research communities via conferences and websites; (4) disseminating the research to the general public, female and minority students by creating Vision OpenHouse events.