Understanding Video of Crowded Environments

拥挤场景的自动监控是当前图像和视频理解技术面临的重大挑战。它在国土安全、自然灾害预防、昆虫行为研究和动物种群监测等领域都有应用。它最近具有强烈的社会意义，因为在涉及大量人群的事件中可能发生恐怖袭击，这是一个目前没有有效解决办法的问题。这个项目为自动监控拥挤场景的技术奠定了基础，通过将他们的视频建模为视觉纹理，以随机但可预测的方式变形，以响应某些事件。特别是，该项目旨在产生1)一套由各种拥挤场景产生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)建立在这些模型上的一系列分类器，以设计重要事件的检测器，3)用于人群视频稳定，分割和解析的算法集合，以及4)大型视频示例数据库。这将为评价该领域未来的研究建立一个共同的实验框架。在教育方面，该项目将为本科生和未被充分代表的背景的学生提供研究机会。URL地址为：www.svcl.ucsd.edu/crowds

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