CAREER: Stochastic Models for Video-Equipped Intelligent Environments

职业：配备视频的智能环境的随机模型

• 批准号: 0237913
• 负责人: Vera Kettnaker
• 金额: $ 40.07万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-15 至 2005-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0237913&HistoricalAwards=false
• 关键词: CAREER; Stochastic; Models; Video; Equipped

A surprisingly large number of safety tasks can be viewed as the detection of unusual events. This research will develop video analysis systems that can distinguish ``unusual'' from ``normal'' activities without specifying the unusual events ahead of time. This stands in contrast to most current systems which are designed to recognize special, pre-determined events. Three major thrusts of work will make the development of such video safety systems more realistic: (1)The PI will significantly increase the precision of parametric process models by including the event duration and the time of an event. (2)These complex models require more training data, yet only few data points will be available to estimate the time-dependent parameters. Therefore, this research will explore several different ways of estimating accurate models robustly from limited data. (3)Finally, this research will investigate how to use HMMs to detect deviations of the current time-series from the learned model. The theoretical work will be put to practice on a task that will increase the safety of independently living senior citizens. The system will learn a model of the daily habits of the person based on video data from multiple cameras in the apartment. Emergency situations like strokes and falls can then be detected as deviations from the regular behavior, and a care taker can be notified. In the later stages of the project, the new models will also be applied to other safety and traffic tasks.The education component of this award includes the development of extensive tutorials on stochastic modeling techniques which will be made available to the public online, as well as an interdisciplinary seminar on stochastic modeling at RPI.

大量的安全任务可以被视为异常事件的检测。这项研究将开发视频分析系统，可以区分“不寻常”和“正常”的活动，而无需提前说明不寻常的事件。这与大多数当前的系统相反，这些系统被设计为识别特殊的预定事件。三个主要的工作重点将使这种视频安全系统的开发更加现实：（1）PI将通过包括事件持续时间和事件的时间来显着提高参数过程模型的精度。（2）这些复杂的模型需要更多的训练数据，但只有很少的数据点可用于估计时间相关参数。因此，本研究将探索几种不同的方法来从有限的数据中稳健地估计精确的模型。（3）最后，本研究将研究如何使用HALTH来检测当前时间序列与学习模型的偏差。理论工作将付诸实践，以提高独立生活的老年公民的安全。 该系统将根据公寓中多个摄像头的视频数据学习人的日常习惯模型。然后，可以将像中风和福尔斯这样的紧急情况检测为与常规行为的偏差，并且可以通知护理人员。在项目的后期阶段，新模型还将应用于其他安全和交通任务。该奖项的教育部分包括开发广泛的随机建模技术教程，这些教程将在网上向公众提供，以及在RPI举办随机建模跨学科研讨会。