Understanding Video of Crowded Environments

了解拥挤环境的视频

• 批准号: 0534985
• 负责人: Nuno Vasconcelos
• 金额: --
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-11-15 至 2009-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534985&HistoricalAwards=false
• 关键词: Understanding; Video; Crowded; Environments

The automated monitoring and surveillance of crowded scenes is a remarkable challenge for current image and video understanding technology. It has application in areas such as homeland security, natural disaster prevention, research on insect behavior, and monitoring of animal populations, among others. It has recently acquired strong societal significance, due to the possibility of terrorist attacks on events involving large concentrations of people, a problem for which there are currently no effective solutions. This project lays the foundation for the technology that will enable the automated monitoring and surveillance of crowded scenes, by modeling their video as a visual texture that deforms itself in stochastic but predictable ways, in response to certain events. In particular, the project aims to produce 1) a suite of generative probabilistic models for the video produced by various types of crowded scenes, and optimal algorithms for the estimation of their parameters, 2) a family of classifiers that build on these models to design detectors of important events, 3) a collection of algorithms for crowd video stabilization, segmentation, and parsing, and 4) a large database of video examples, that will establish a common experimental framework for the evaluation of future research in the field. Educationally, the project will provide research opportunities to both undergraduate students and students of underrepresented backgrounds.The URL address is: www.svcl.ucsd.edu/crowds

对于当前的图像和视频理解技术，对拥挤场景的自动监视和监视是一个非凡的挑战。 它在国土安全，预防自然灾害，昆虫行为研究以及对动物种群的监测等领域有应用。 由于可能对涉及大量人群的事件遭受恐怖袭击，目前尚无有效的解决方案，因此它最近获得了强大的社会意义。 该项目通过将其视频建模为视觉纹理，以随机但可预测的方式对某些事件形成自身的视觉纹理，从而实现了对拥挤场景的自动监视和监视的基础。特别是，该项目旨在生产一套由各种类型的拥挤场景制作的视频的生成概率模型，以及用于估计其参数的最佳算法，2）基于这些模型的一系列分类器，以设计重要事件的探测器，以设计大量的视频稳定，并构建视频稳定，并构建a exters a a afters a a emants a a sembersation，and semplassept，and parssect，and parssement and parssement＆parssement and parssement and parssement and par semply，以及4岁）。评估该领域未来研究的常见实验框架。在教育上，该项目将为本科生和代表性不足的学生提供研究机会。URL地址为：www.svcl.ucsd.edu/crowds