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.

该项目开发基于分布式消息传递的高效计算机视觉算法，用于解决人群场景分析任务，例如检测和跟踪近距离个体。这些和其他视觉任务可以表述为离散组合优化问题，例如二元线性或二次规划，并且研究其底层数学结构可以产生见解，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元线性规划问题的消息传递的正确性，正在被用来开发用于人群场景分析的实用视觉算法，并扩展到开发为更困难的问题找到良好近似解决方案的算法。项目团队还在探索基于随机化和将大规模问题分解为相互关联的子任务集合的近似推理方法，这些子任务可以更有效地并行解决。 自动视觉系统可以持续监控拥挤的公共空间，提供人群密度的实时态势感知，并检测危险行为或偏离正常交通流的早期迹象。在人群中跟踪个人并检测人群互动的能力可应用于国土安全、执法和国防领域。该项目的成果将通过与其他学者合作、发表同行评审文章、在专业会议上演讲、将课程模块引入研究生和本科生计算机科学课程以及公开发布源代码来传播。