Smart video compression

智能视频压缩

• 批准号: 473111-2014
• 负责人: Lang, Jochen
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=564917
• 关键词: Smart; video; compression

This research is a collaborative effort between a research team at the University of Ottawa and a Canadian industrial partner, Thales Defence and Security Canada. Thales Canada is the prime contractor for the Land Command Support Systems (LCSS) providing long-term software design, development and integration support of the Canadian Armed Forces' core command and control system. It has developed a solution for video surveillance with a vehicle but in the field the video needs to be compressed to be sent back quickly to headquarters and to be stored over the long term. Standard video compression technology is not suitable because the required compression ratios cannot be achieved without losing important information. The proposed research project will develop new solutions with video summary techniques based on content analysis, user interests and long-term detection, tracking and learning. These techniques can achieve much higher compression ratios than general purpose video encoders because the overwhelming number of frames in a surveillance video record nothing but uninteresting background. The expertise of the University of Ottawa researchers in saliency models, content aware compression and tracking will enable the rapid development of software technology for content-aware video compression and summary based on user interactions. This software will be developed at the University of Ottawa in tight collaboration with Thales' Ottawa Office and delivered to Thales Canada at the end of this project.

这项研究是渥太华大学的一个研究团队与加拿大工业合作伙伴泰利斯防务和安全加拿大公司合作进行的。泰利斯加拿大公司是陆地指挥支持系统(LCS)的主承包商，为加拿大武装部队的核心指挥和控制系统提供长期的软件设计、开发和集成支持。它开发了一种用车辆进行视频监控的解决方案，但在外地，需要对视频进行压缩，以便迅速送回总部并长期存储。标准的视频压缩技术并不适用，因为要达到所需的压缩比，就必须丢失重要的信息。拟议的研究项目将开发基于内容分析、用户兴趣和长期检测、跟踪和学习的视频摘要技术的新解决方案。这些技术可以实现比通用视频编码器高得多的压缩比，因为监控视频中压倒性的数量的帧记录的只是乏味的背景。渥太华大学研究人员在显著模型、内容感知压缩和跟踪方面的专业知识将使基于用户交互的内容感知视频压缩和摘要的软件技术得以快速发展。该软件将由渥太华大学与泰利斯公司渥太华办事处紧密合作开发，并在该项目结束时交付给泰利斯加拿大公司。