CAREER: Stochastic Models for Video-Equipped Intelligent Environments

数量惊人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，该系统可以区分“异常”和“正常”活动，而无需提前指定异常事件。这与大多数旨在识别特殊的、预先确定的事件的当前系统形成鲜明对比。三个主要的工作重点将使此类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时间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，从有限的数据中稳健地估计准确的模型。(3)最后，本研究将探讨如何使用hmm来检测当前时间序列与学习模型的偏差。理论工作将被付诸实践，以提高独立生活的老年人的安全。该系统将根据公寓内多个摄像头的视频数据学习一个人的日常习惯模型。像中风和跌倒这样的紧急情况可以被检测为与正常行为的偏差，并可以通知护理人员。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举办一个关于随机建模的跨学科研讨会。

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.