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

职业：高维运动的可视化分析：分布式/协作方法

• 批准号: 0347877
• 负责人: Ying Wu
• 金额: $ 47.5万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0347877&HistoricalAwards=false
• 关键词: CAREER; Visual; Analysis; Dimensional; Motion

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.

该项目旨在分析视频中的高维运动（HDM）。 HDM指的是具有高度自由度的各种复杂运动，包括人体的表达，弹性形状的变形和多种遮挡靶标的多运动。该项目的目的是通过系统地采用新的分布式/协作方法来克服这个具有挑战性的视觉推理问题中嵌入的维度的诅咒，该方法统一了各种HDM。新方法与集中式方法大不相同，将HDM分布在基于Markov网络模型的子部分运动的网络表示中。那么，正如该模型对拟议的理论研究所揭示的那样，通过分布式但相互约束的小规模的视觉推理过程中的“协作”可以有效，有效地实现高度的HDM推理任务，并由拟议的协作粒子网络算法实施。预计这种新方法将明显更有效，更可扩展和灵活，并且更健壮。该项目通过使人类的追踪和检测技术成为可能，从而对智能视频监视产生了影响，并显着使人类计算机互动和医学成像的研究有益于研究。该研究与旨在通过（1）开发一项集成课程的教育活动有关，以促进学习和创新，以进行视觉计算和统计模型模型； （2）激励学生通过创新的课程项目和现实世界应用探索未知的前沿； （3）通过会议和网站向其他相关研究社区推出； （4）通过创建视觉开放式活动将研究传播给公众，女性和少数族裔学生。