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: 1551290
• 负责人: Deva Ramanan
• 金额: $ 10.6万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551290&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其活动的系统。人类的行为很少是孤立的--周围的环境、附近的物体和附近的人都会影响所执行的活动的性质，例如“吃东西”和“握手”。“这个项目的研究目标是接近人类在理解由人-物和人-人交互定义的活动视频方面的表现。这个项目利用结构化的上下文表示来预测给定的时空数据。它这样做是通过扩展最近成功的工作对象识别的时空域，引入扩展时空分组和上下文建模。视频可以提取额外的动态线索中没有静态图像，但这会带来额外的计算负担，通过近似解析和大规模的判别式learning.To把活动识别在公司的定量地面上的算法创新，提出的模型进行评估，使用具体的指标的基础上的日常生活活动（ADL）和人类的记忆模型从医学和人类学社区。例如，用于自动监控中风患者与日常物品互动的系统，以及在紧急演习期间自动分析危机响应团队互动的系统。该项目产生了非脚本的，真实世界的，标记的动作识别数据集，有利于整个研究界。