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 -9900115ZABIH，Ramincornell University $ 150,000-36 MOS.DYNAGINIC上下文识别移动对象，这是三年的标准奖励。该研究将解决重建所有移动对象所采取的路径的问题，这些对象在环境中从多个非重叠摄像机中暂时可见。在许多有趣的监视应用程序中，重要的是要整合来自多个摄像机的信息。不幸的是，人类操作员同时参加几个视频流是非常困难的。 PI将采用贝叶斯方法，其中最佳解决方案是具有最高后概率的对象路径的集合。 PI计划调查组合量限量方法的使用，包括线性编程和剪图剪切，以设计这些问题的设计应用程序。这些方法有望产生有效的算法，并具有有趣的保证，以确保近似值的质量。