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

这个项目是关于分析视频中的高维运动(HDM)。HDM是指各种复杂的高自由度运动，包括人体的关节、弹性形状的变形以及多个遮挡目标的多个运动。该项目的目标是通过系统地寻求一种新的分布式/协作方法来统一各种HDMS，以克服嵌入在这一具有挑战性的视觉推理问题中的维度诅咒。与集中式方法有很大不同的是，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。该模型的理论研究和协同粒子网络算法的实现表明，分布式但相互约束的小规模视觉推理过程之间的“协作”可以有效地完成禁止的HDM推理任务。预计这一新方法将显著提高效率、可扩展性和灵活性，并更加健壮。该项目通过实现快速、准确的人体跟踪和检测技术，对智能视频监控产生影响，并显著有利于人机交互和医学成像的研究。研究与旨在促进学习和创新的教育活动联系在一起，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应用程序激励学生探索未知的前沿；(3)通过会议和网站向其他相关研究社区延伸；(4)通过创建Vision OpenHouse活动向普通公众、女性和少数族裔学生传播研究成果。

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.