RI: Medium: Integrated Analysis and Synthesis for Data Mining in a Video Network

RI：媒介：视频网络中数据挖掘的集成分析与综合

• 批准号: 0905671
• 负责人: Bir Bhanu
• 金额: $ 120万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905671&HistoricalAwards=false
• 关键词: RI; Medium; Integrated; Analysis; Synthesis

The University of California-Riverside is awarded a grant to develop a new research para-digm in which both analysis-by-synthesis and synthesis-by-analysis are integrated in a closed-loop learning framework for developing robust, scalable systems for video mining in network of cameras. This paradigm allows a principled transition from limited domains to large-scale de-ployment. The collaborative project brings together three institutions (UCR, UCLA, and SUNY-SB) for aggregating and interpreting information and discovering patterns of human behavior from multiple video streams and evaluating them in realistic virtual and real-life scenarios such as video surveillance, traffic monitoring, and elderly care.The project introduces four novel elements. First, it develops methods for incremental model-ing and scaling of Bayesian nets for videos and glues together the analysis and synthesis. Sec-ond, it employs multiple strategies based on game theory and a multi-objective optimization framework for cooperative and distributed on-line control of active cameras. Third, it uses multi-ple representations in a framework of hierarchical Bayesian and Markov random fields and sta-tistical tensor models for learning long-term models of activities. Finally, it involves new models based on dynamical systems theory for seamless tracking and recognition in a video network. The project blends the theoretical and algorithmic contributions with the development of a prototype that integrates a network of video cameras with a virtual vision simulator which incorporates sophisticated artificial life models of humans. It builds increasingly sophisticated models of humans, vehicles, context, illumination, texture, shape, and motion over time. The software tools integrating the analysis and synthesis are widely disseminated.

加利福尼亚大学河畔大学获得了一项赠款，以开发一项新的研究，其中逐一分析和按分析的合成均集成在一个闭环学习框架中，以开发摄影机网络中的可扩展，可扩展的视频挖掘系统。该范式允许从有限域到大规模下降雇用的原则过渡。 该协作项目汇集了三个机构（UCR，UCLA和SUNY-SB），用于汇总和解释信息，并从多个视频流中发现人类行为的模式，并在现实的虚拟和现实生活中评估它们，例如视频监测，交通监控以及老年人护理。首先，它开发了贝叶斯网的增量模型和缩放的方法，以将视频和胶合胶合在一起分析和合成。 Sec-ond，它采用了基于游戏理论的多种策略，以及一个多目标优化框架，用于合作和分布式的在线摄像机控制。第三，它在分层贝叶斯和马尔可夫随机字段以及Sta-tister张量模型的框架中使用多个表示形式来学习活动的长期模型。最后，它涉及基于动态系统理论的新模型，用于视频网络中的无缝跟踪和识别。该项目将理论和算法的贡献与开发原型的开发相结合，该原型将摄像机网络与虚拟视觉模拟器集成在一起，该模拟器结合了人类的复杂人工生命模型。它构建了人类，车辆，上下文，照明，纹理，形状和运动的越来越复杂的模型。集成分析和合成的软件工具被广泛传播。