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，Ramin康奈尔大学 150,000 美元 - 36 个月。移动物体的动态上下文识别这是一个为期三年的标准奖项。该研究将解决重建从环境中的多个非重叠摄像机暂时可见的所有移动物体所采取的路径的问题。在许多有趣的监控应用中，集成来自多个摄像机的信息非常重要。不幸的是，对于操作人员来说，同时处理多个视频流是非常困难和累人的。 PI 将采用贝叶斯方法，其中最佳解决方案是给定观测数据后验概率最高的对象路径集。 PI 计划研究组合优化方法（包括线性规划和图割）的使用，为这些问题设计近似方案。这些方法有望产生有效的算法，并对近似的质量提供有趣的保证。