Interpreting Human Behaviour in Video using FSA's and Object Context

使用 FSA 和对象上下文解释视频中的人类行为

• 批准号: 0534837
• 负责人: David Forsyth
• 金额: --
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534837&HistoricalAwards=false
• 关键词: Interpreting; Human; Behaviour; Video; using

PI: David ForsythTitle: Interpreting Human Behavior in Video using FSA's and Object ContextUnderstanding what people are doing in a video is one of the great unsolved problems of computer vision. A fair solution opens tremendous application possibilities. The proposed work will use existing tools from the speech and object recognition community --- in particular, finite state automata or FSA's --- to obtain an understanding of activities that depend on detailed information about the body.The particular focus is everyday activity. In this case, a fixed vocabulary either doesn't exist, or isn't appropriate. For example, one does not know words for behaviors that appear familiar. One way to deal with this is to work with a notation (for example, laban notation); but such notations typically work in terms that are difficult to map to visual observables (for example, the weight of a motion). The alternatives are either to develop a vocabulary, or to develop expressive tools for authoring models.This project will explore the third approach of building tools for authoring models of behavior quickly and expressively using finite-state methods. Research will explore a class of models that are easy to author from existing, or easily available, data. The interpretation of what someone is doing is affected by the objectsnearby --- a person standing near a bus stop is doing something different from a person standing near an office door. The models studied make it practical to investigate this phenomenon of object context, using recent advances from the object recognition literature.Evaluating models for everyday behaviors is hard, because there is no prospect of obtaining a large collection of marked up video (among other things, there isn't a vocabulary in which to mark it up). This project will use proxies --- statistics that are hard to measure from video without accurate inferences of behavior, but easy to measure in other ways --- to evaluate behavior representations. These will make it possible to tell whether, for example, a model of buying a beverage represents the concept accurately.Intellectual merits: This project will produce very large finite state models of behavior using the same hierarchical authoring methods used in speech. There will be a particular emphasis on behaviors which require one to understand the kinematic configuration of the body, a topic that has been very difficult to study to date, with an intention of identifying basic building blocks of a vocabulary of everyday behavior. The results should include datasets of public behavior that can be disseminated, without encountering privacy concerns. New insights into the structure of human motion and behavior should emerge from (a) observations of people in public; (b) the process of authoring models; and (c) methods for identifying and modelling compositional structure in motion.Broader impact: This project should make substantial progress on one of the key open and applicable problems in computer vision. Methods that can search video for particular behaviors and compute statistics of behaviors have a wide range of applications, including human-computer interfaces built around computers that can watch the body; an improved understanding of what people do in public which will result in better architectural planning; more efficient management of surveillance data, allowing searches for dangerous behaviors while preserving privacy. Education and access: This project will contribute to the graduate training of several students, and work described will contribute to a planned text on computing with human motion.URL: http://luthuli.cs.uiuc.edu/~daf/action.html

PI：David ForsythTitle：使用FSA和对象上下文解释视频中的人类行为理解视频中的人在做什么是计算机视觉中尚未解决的重大问题之一。一个公平的解决方案打开了巨大的应用可能性。这项拟议的工作将使用语音和对象识别领域的现有工具-特别是有限状态自动机或FSA--来了解依赖于身体详细信息的活动。特别关注日常活动。在这种情况下，固定的词汇表要么不存在，要么不合适。例如，一个人不知道用词来描述看起来熟悉的行为。解决这一问题的一种方法是使用符号(例如，Laban符号)；但这种符号通常以难以映射到可视可见项(例如，运动的权重)的术语工作。另一种选择是开发词汇表，或者开发用于创作模型的表达工具。本项目将探索第三种方法，即使用有限状态方法快速且富有表现力地构建行为模型的创作工具。研究将探索一类易于根据现有或容易获得的数据创建的模型。对某人正在做的事情的解释会受到附近物体的影响-站在公共汽车站附近的人做的事情与站在办公室门口的人做的事情不同。所研究的模型使得使用对象识别文献的最新进展来研究对象背景的这种现象变得可行。评估日常行为的模型是困难的，因为不可能获得大量的标记视频集合(其中，没有一个词汇可以标记它)。这个项目将使用代理-统计数据，如果没有准确的行为推断，很难从视频中衡量，但很容易通过其他方式衡量-评估行为表征。例如，这将使判断购买饮料的模型是否准确地代表了概念成为可能。智力上的优点：这个项目将使用语音中使用的相同的分层创作方法来生成非常大的有限状态行为模型。将特别强调需要了解身体运动学配置的行为，这是一个迄今为止很难研究的话题，目的是识别日常行为词汇的基本组成部分。结果应该包括可以传播的公共行为数据集，而不会遇到隐私问题。对人类运动和行为结构的新见解应该来自(A)对公众的观察；(B)创作模型的过程；以及(C)识别运动中的组成结构并对其建模的方法。广泛的影响：该项目应该在计算机视觉中的一个关键的公开和适用的问题上取得实质性进展。可以搜索特定行为的视频和计算行为统计数据的方法有广泛的应用，包括围绕可以观察身体的计算机构建的人机界面；对人们在公共场合做什么的更好理解，这将导致更好的建筑规划；更高效的监控数据管理，允许搜索危险行为，同时保护隐私。教育和访问：该项目将有助于对几名学生进行研究生培训，所述工作将有助于编写一篇关于人类运动计算的计划文本。网址：http://luthuli.cs.uiuc.edu/~daf/action.html