RI: Small: Convex Architecture for Human Movement Understanding

该项目的重点是用于人类运动理解的凸方法，即估计和量化视频中的人类运动和运动的任务。与以前的方法相比，凸方法显式地建模复杂的组件间的相关性和全局约束，并能够实现更可靠的结果。使用学习方法自动生成的凸公式。利用它们的特殊结构，设计了有效的算法。该项目从不同角度研究人体运动理解，并提供全局运动估计，身体部位跟踪和局部补丁运动轨迹估计的凸解。主要研究内容包括凸关节图匹配与目标候选集的大小解耦的复杂性，凸结构学习和解决大规模问题的有效分解方法。通过稳健的运动估计，该项目进一步解决了性能识别，这是一种量化行为风格和性能的新应用。该项目有利于监控，机器人，人机交互，娱乐，体育和医疗应用。在这个项目中开发的凸框架也可以扩展到许多其他领域，以构建信息检索，控制系统，调度和网络分析的优化模型，并解决运筹学和仿真中的大规模问题。该项目开发了一种用于理解人类运动的新技术;还开发了一个基准数据集。研究结果通过同行评审的出版物、网页下载和大学课程传播。

This project focuses on convex methods for human movement understanding, the task of estimating and quantifying human motion and movement in videos. Compared with previous approaches, the convex methods explicitly model complex inter-component correlations and global constraints and are able to achieve more reliable results. The convex formulations are automatically generated using learning methods. By taking advantage of their special structures, efficient algorithms are devised. This project studies human movement understanding from different perspectives and provides convex solutions to global movement estimation, body part tracking, and local patch motion trajectory estimation. The key research components include convex articulated graph matching with complexity decoupled from the sizes of target candidate sets, convex structure learning and efficient decomposition methods for solving large-scale problems. With robust movement estimation, this project further addresses performance recognition, a new application that quantifies the style and performance of actions.This project benefits surveillance, robotics, human computer interaction, entertainment, sports and medical applications. The convex framework developed in this project can also be extended to many other areas to construct optimal models for information retrieval, control systems, scheduling and network analysis, and to solve large-scale problems in operational research and simulation. This project develops a new technology for understanding human movements; a benchmark dataset is also developed. The results are disseminated by peer reviewed publications, web page downloads, and by university courses.