SBIR Phase I: Semantic Video Analysis for Video Summarization and Recommendation

这个小企业创新研究（SBIR）第一阶段项目的广泛影响/商业潜力是相当可观的，因为各种互补的新技术正在引领一个新的时代，在这个时代，视觉信息正在成为与文本和语音一样的一流媒体类型。今天，业余和专业的摄像师仍然必须进入虚拟的暗室来筛选视频，编辑它，并制作引人入胜的内容。视频创作正在等待宝丽来的时刻，当一个技术解决方案将改变后期制作所需的时间来创造引人入胜的视频。如果成功，该项目开发的技术将大大提高任何视频捕捉设备的实用性，并将在生活记录和知识转移等互联网媒体以外的领域产生影响。如今，无数重要或令人难忘的事件的视频片段通常被归档和遗忘，取而代之的是，它们可以被自动总结，并以可用和吸引人的格式提供。这项小型企业创新研究（SBIR）第一阶段项目旨在评估基于神经网络并适应人类心理测量的自动视频摘要系统的技术可行性。随着人们录制的视频数量超过了他们的消费能力（视频泛滥问题），一种能够自动将原始视频转换为相关且引人入胜的摘要的技术变得越来越重要。该公司提出的平台将简化视频分享、搜索和观看，所有这些都是我们在线生活的主要内容。从科学上讲，我们正处于人工视觉系统能力的独特时期，一些系统在有限的领域可以与人类的表现相媲美。此外，视觉心理学领域在将视觉语义信息与认知现象（如图像记忆）联系起来方面也取得了进展。综上所述，现在有可能自动预测视觉信息的认知相关性，并产生有效的视频摘要。该项目结合了用于视觉对象识别的深度神经网络，用于上下文嵌入时间信息的循环网络，以及用户兴趣、记忆性和独特性的测量。主要的技术目标是确定系统是否可以自动预测人工制作的视频摘要。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is considerable because a variety of complementary new technologies is ushering in a new era in which visual messages are becoming a first-class media type along-side text and speech. Today, both amateur and professional videographers still have to enter the virtual darkroom to sift through video, edit it, and produce engaging content. Video creation is waiting for its Polaroid moment, when a technological solution will transform the post-production time required to create engaging video. If successful, the technology developed in this project will greatly increase the utility of any video capture device and would have implications outside of Internet media in areas such as life recording and knowledge transfer. The countless video clips of important or memorable events that are today commonly archived and forgotten could instead be automatically summarized and made available in a usable and engaging format. This Small Business Innovation Research (SBIR) Phase I project aims to evaluate the technical viability of an automatic video summarization system based on neural networks and adapted to measurements of human psychology. As people collectively record more videos than they can possibly consume (the video deluge problem), a technology that automatically turns raw videos into relevant and engaging summaries becomes increasingly critical. The company's proposed platform would streamline video sharing, search, and viewing, all of which are staples of our online lives. Scientifically we are at a unique time in the capabilities of artificial visual systems, with some systems rivaling human performance in limited domains. Furthermore, the field of visual psychology has also seen recent progress in relating visual semantic information to cognitive phenomena, like memorability of images. Taken together, it may now be possible to automatically predict the cognitive relevance of visual information and produce effective video summarizations. This project combines deep neural networks for visual object recognition, recurrent networks for contextually embedded temporal information, and user measurement of interest, memorability, and uniqueness. The primary technical objective is to determine whether a system can automatically predict human-produced video summarizations.