RI: Small: Distributed Combinatorial Optimization for Crowd-Scene Analysis

RI：小型：人群场景分析的分布式组合优化

• 批准号: 1218729
• 负责人: Robert Collins
• 金额: $ 45万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218729&HistoricalAwards=false
• 关键词: RI; Small; Distributed; Combinatorial; Optimization

This project develops efficient computer vision algorithms based on distributed message passing for solving crowd-scene analysis tasks such as detection and tracking of closely spaced individuals. These and other vision tasks can be formulated as discrete combinatorial optimization problems, e.g., binary linear or quadratic programs, and studying their underlying mathematical structure can yield insights that allow larger and more challenging problem instances to be addressed. Recent theoretical work proving the correctness of message passing for some classes of binary linear programming problems is being leveraged to develop practical vision algorithms for crowd scene analysis and extended to develop algorithms for finding good approximate solutions to harder problems. The project team is also exploring approximate inference methods based on randomization and on decomposition of large-scale problems into collections of interrelated subtasks that can be solved more efficiently and in parallel. Automated vision systems can continuously monitor crowded public spaces to provide real-time situational awareness of crowd density and to detect early signs of dangerous behavior or deviations from normal traffic flow. The ability to track individuals through a crowd and to detect the interactions of groups of people has applications in the areas of homeland security, law enforcement, and defense. Results from this project will be disseminated through collaboration with other scholars, publication of peer-reviewed articles, presentations at professional meetings, introduction of course modules into the graduate and undergraduate computer science curriculum, and through public release of source code.

该项目基于分布式消息传递来开发有效的计算机视觉算法，以解决人群分析任务，例如检测和跟踪紧密间隔的个体。这些和其他视觉任务可以作为离散的组合优化问题（例如二进制线性或二次程序程序）提出，并研究其潜在的数学结构可以产生洞察力，从而可以解决更大，更具挑战性的问题实例。 最近的理论工作证明了某些类别的二进制线性编程问题的消息传递的正确性正在利用以开发用于人群场景分析的实用视觉算法，并扩展到开发算法，以找到解决困难问题的良好解决方案。项目团队还基于随机化和大规模问题分解为相互关联的子任务的集合来探索近似推理方法，这些方法可以更有效，并行解决。 自动化视觉系统可以连续监视拥挤的公共场所，以提供对人群密度的实时情境意识，并发现危险行为的早期迹象或偏离正常交通流量。在人群中追踪个人并检测人群相互作用的能力在国土安全，执法和国防领域具有应用。该项目的结果将通过与其他学者的合作，同行评审的文章的出版，专业会议上的演讲，将课程模块介绍给研究生和本科计算机科学课程以及源代码的公开发布。