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

该项目开发了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，例如检测和跟踪密集的个体。这些和其他视觉任务可以被公式化为离散的组合优化问题，例如，二进制线性或二次规划，并研究其基本的数学结构可以产生见解，允许更大和更具挑战性的问题实例得到解决。 最近的理论工作证明的正确性的消息传递的某些类别的二进制线性规划问题正在利用开发实用的视觉算法的人群场景分析和扩展到开发算法，寻找良好的近似解决方案，更难的问题。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，以提供对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量的早期迹象。在人群中跟踪个人和检测人群交互的能力在国土安全、执法和国防领域都有应用。该项目的成果将通过与其他学者合作、发表同行评议文章、在专业会议上发言、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等方式传播。

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.