Understanding Video of Crowded Environments

拥挤场景的自动监控和监视是当前图像和视频理解技术的一个显著挑战。 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。 最近，由于恐怖主义分子可能对涉及大量人群聚集的活动发动袭击，这一问题具有很大的社会意义，目前还没有有效的解决办法。 该项目为实现拥挤场景的自动监控和监视的技术奠定了基础，通过将其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）一套生成概率模型的视频产生的各种类型的拥挤的场景，和最佳算法的参数估计，2）一个家庭的分类器，建立在这些模型设计检测器的重要事件，3）算法的集合人群视频稳定，分割和解析，以及4）视频实例的大型数据库，这将为该领域未来研究的评估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代表性不足的背景的学生提供研究机会。URL地址是： 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