CAREER: Discriminative Spatiotemporal Models for Recognizing Humans, Objects, and their Interactions

职业：识别人类、物体及其交互的判别时空模型

• 批准号: 0954083
• 负责人: Deva Ramanan
• 金额: $ 44.45万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2015-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0954083&HistoricalAwards=false
• 关键词: CAREER; Discriminative; Spatiotemporal; Models; Recognizing

One of the goals of computer vision is to build a system that can see people and recognize their activities. Human actions are rarely performed in isolation -- the surrounding environment, nearby objects, and nearby humans affect the nature of the performed activity.Examples include actions such as "eating" and "shaking hands." The research goal of this project is to approach human performance in understanding videos of activities defined by human-object and human-human interactions.This project makes use of structured, contextual representations to make predictions given spatiotemporal data. It does so by extending recent successful work on object recognition to the space-time domain, introducing extensions for spatiotemporal grouping and contextual modeling. Video enables the extraction of additional dynamic cues absent in static images, but this poses additional computational burdens that are addressed through algorithmic innovations for approximate parsing and large-scale discriminative learning.To place activity recognition on firm quantitative ground, the proposed models are evaluated using concrete metrics based on activities of daily living (ADL) and human proxemic models from the medical and anthropological communities. Examples include systems for automated monitoring of stroke patients interacting with everyday objects and automated analysis of crisis response team interactions during emergency drills. This project produces non-scripted, real-world, labeled action recognition datasets, of benefit to the research community as a whole.

计算机视觉的目标之一是建立一个可以看到人并识别他们的活动的系统。人类的行为很少是孤立进行的——周围的环境、附近的物体和附近的人都会影响所进行的活动的性质。示例包括“吃东西”和“握手”等动作。该项目的研究目标是了解人类在理解人与物体和人与人交互定义的活动视频方面的表现。该项目利用结构化的上下文表示来根据时空数据进行预测。它通过将最近在对象识别方面的成功工作扩展到时空域，引入时空分组和上下文建模的扩展来实现这一目标。视频能够提取静态图像中缺少的额外动态线索，但这会带来额外的计算负担，可以通过近似解析和大规模判别学习的算法创新来解决。为了将活动识别置于坚实的定量基础上，使用基于日常生活活动（ADL）的具体指标以及来自医学和人类学界的人类邻近模型来评估所提出的模型。例子包括自动监测中风患者与日常物体互动的系统，以及自动分析紧急演习期间危机应对团队互动的系统。该项目生成非脚本、真实世界、标记的动作识别数据集，对整个研究界都有好处。