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.

这个i-Corps项目的更广泛的影响/商业潜力涉及到计算机视觉分析视频，使用视觉和听觉线索来创建内容的描述。这项技术具有从执法到监控再到消费者应用的大量潜在应用。其中包括实现大量相机数据的高效存储和检索。智能监控系统可以通过允许将一整天的视频片段汇总为与安全相关的事件列表的功能来增强。该技术还可以自动组织大量多媒体数据。这个I-Corps项目涉及计算机视觉技术的商业化可行性研究，以自然语言文本和语法，即语义来表达视频内容。这个项目建立在一个视频分析框架的基础上，该框架利用最先进的方法，以一种被称为模式理论的数学和统计方法对统一的形式主义进行编码，用于对象检测和动作识别。视频分析方法可以(I)在利用容易获得的本体论信息的同时不需要大量训练数据来处理复杂事件的结构可变性，(Ii)克服动作和对象的机器学习分类器的分类错误，(Iii)适应场景杂乱，即不在场景中存在的活动中的无关对象，(Iv)并且管理基本事件的序列，所有这些都不需要重新训练。形式主义允许轻松地合并时间、空间和逻辑约束。该团队已经在标准数据集上演示了这一系统，该数据集用于对人类活动识别任务的计算机视觉性能进行基准测试。