Dynamic Contextual Recognition of Moving Objects

移动物体的动态上下文识别

• 批准号: 9900115
• 负责人: Ramin Zabih
• 金额: $ 15万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9900115&HistoricalAwards=false
• 关键词: Dynamic; Contextual; Recognition; Moving; Objects

Abstract IIS-9900115Zabih, RaminCornell University $150,000 - 36 mos.Dynamic Contextual Recognition of Moving ObjectsThis is a standard award of three years. The research will address the problem of reconstructing the paths taken by all moving objects that are temporarily visible from multiple non-overlapping cameras in an environment. There are many interesting surveillance applications where it is important to integrate information from multiple cameras. Unfortunately, it is very difficult and tiring for human operators to simultaneously attend to several video streams. The PI will take a Bayesian approach, where the optimal solution is the set of object paths with the highest posterior probabilitygiven the observed data. The PI plans to investigate the use of combinatorialoptimization methods, including linear programming and graph cuts, to designapproximation schemes for these problems. These methods are expected to result in efficient algorithms with interesting guarantees concerning thequality of the approximation.

摘要IIS-9900115 Zabih，RaminCornell大学$150，000 - 36 mos。移动对象的动态上下文识别这是一个三年的标准奖项。该研究将解决重建所有移动物体所采取的路径的问题，这些物体在环境中从多个非重叠摄像机暂时可见。有许多有趣的监控应用，其中重要的是集成来自多个摄像头的信息。不幸的是，这是非常困难和累人的人类操作员同时参加几个视频流。PI将采用贝叶斯方法，其中最优解是给定观测数据的具有最高后验概率的对象路径集。 PI计划研究使用组合优化方法，包括线性规划和图形切割，为这些问题设计近似方案。这些方法预计将导致有效的算法与有趣的保证有关的近似质量。