I-Corps: Semantic Video - from Video to Descriptions

I-Corps：语义视频 - 从视频到描述

• 批准号: 1647887
• 负责人: Sudeep Sarkar
• 金额: $ 5万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1647887&HistoricalAwards=false
• 关键词: Corps; Semantic; Video; Descriptions

The broader impact/commercial potential of this I-Corps project involves computer vision analysis of video, using both visual and auditory cues, to create descriptions of the content. The technology has a large variety of potential applications from law enforcement to surveillance to consumer applications. These include enabling the efficient storage and retrieval of large volumes of camera data. Smart surveillance systems can be enhanced with features that allows for summarization of daylong video footages as a list of security-relevant events. The technology can also allow automated organization of large collections of multimedia data.This I-Corps project involves commercialization feasibility research for a computer vision technology for expressing video content in terms of natural language text and grammar, i.e. semantics. This project builds on a video analysis framework that leverages state-of-the-art methods for object detection and action recognition in a unified formalism encoded in terms of a mathematical and statistical approach known as pattern theory. The video analysis approach can (i) handle structural variability of complex events without requiring large training data while exploiting easily available ontological information, (ii) overcome classification errors of machine learning classifiers of actions and objects, (iii) accommodate scene clutter, i.e. extraneous objects that do not in the activity present in the scene, (iv) and manage sequences of elementary events, all without retraining. The formalism allows for the easy incorporation of temporal, spatial, and logical constraints. This team has demonstrated this system on standard datasets used to benchmark performance in computer vision for human activity recognition tasks.

该I-Corps项目的更广泛的影响/商业潜力涉及使用视觉和听觉提示对视频进行计算机视觉分析，以创建内容的描述。该技术具有从执法到监视到消费者应用的各种潜在应用。其中包括实现大量相机数据的有效存储和检索。 智能监视系统可以通过允许将长时间视频素材汇总为相关事件列表的功能来增强智能监视系统。 该技术还可以允许自动组织大量的多媒体数据集合。本I-Corps项目涉及计算机视觉技术的商业化可行性研究，用于以自然语言文本和语法（即语法）来表达视频内容。该项目以视频分析框架为基础，该框架利用最新的方法在用数学和统计方法（称为模式理论）编码的统一形式主义中以对象检测和行动识别。视频分析方法可以（i）处理复杂事件的结构性变异性，而无需大量的培训数据，同时利用易于可用的本体论信息，（ii）克服机器学习和对象的机器学习分类器的分类错误，（iii）适应场景杂乱无章，即在场景中不存在的活动中不存在的对象（iv）和管理基本事件的序列，而无需重新训练。形式主义允许轻松纳入时间，空间和逻辑约束。该团队已在用于在人类活动识别任务的计算机愿景中基于计算机愿景的标准数据集上演示了该系统。