SBIR Phase I: Machine Vision for Content-based Video Marketing Analytics

SBIR 第一阶段：用于基于内容的视频营销分析的机器视觉

• 批准号: 1621689
• 负责人: Samuel Anthony
• 金额: $ 22.46万
• 依托单位: Perceptive Automata, Inc.
• 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: 2016-07-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1621689&HistoricalAwards=false
• 关键词: SBIR; Phase; Machine; Vision; Content

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to protect consumer privacy while continuing to enable the ad-supported Internet model. Current tracking-based consumer targeting approaches inherently erode consumer privacy, surreptitiously tracking users across many different web sites in an effort to gather demographic and behavioral data. On the flip side of the coin, marketers need to collect such data to successfully reach their audiences, and the revenue that marketers pour into advertising online has become an essential component of the economics of the internet. Today, this delicate balance of competing pros and cons is further threatened by the rise of ad-blocking software, which erodes the value of internet ad placement. The video marketing analytics capability developed in this project will limit marketers' need for invasive consumer data, while improving consumer experience. In the commercial realm, marketers would value the opportunity to target their ads in the most emotionally consonant, least disruptive, and most engaging manner possible. This technology will provide marketers with the capability to watch millions of videos algorithmically, thus enabling a more streamlined and customized viewer experience than has ever before been possible on television or on the Internet. This Small Business Innovation Research Phase I project seeks to develop commercial applications for Perceptual Annotation, a technology developed with NSF funding that allows detailed measurements of human performance to be infused into a machine learning process, allowing the machine learner to both perform better and to perform in a way that is more consistent with humans. By adding this new category of human-derived supervisory signal into a machine learning process, the proposers have demonstrated that it is possible to significantly boost machine vision performance, allowing machines to generalize better to new, previously unseen images. While the company's technology has been rigorously validated on large-scale "in the wild" academic datasets, a major technical drive in the proposed SBIR Phase I activities will be to shift the company's efforts to the analysis of "live," enormous, and ever-expanding data sets such as online videos. A second major drive of the proposed Phase I work will be the construction of "second stage" machine learning models that take perceptual-annotation-based machine ratings as an input and output actionable marketing decisions.

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是保护消费者隐私，同时继续支持广告支持的互联网模式。目前基于跟踪的消费者定位方法固有地侵蚀了消费者隐私，秘密跟踪许多不同网站的用户，以努力收集人口统计和行为数据。另一方面，营销人员需要收集这样的数据才能成功地接触到他们的受众，营销人员投入在线广告的收入已经成为互联网经济的一个重要组成部分。如今，广告拦截软件的兴起进一步威胁到了这种相互竞争的利弊之间的微妙平衡，这种软件侵蚀了互联网广告投放的价值。该项目开发的视频营销分析功能将限制营销人员对侵入性消费者数据的需求，同时改善消费者体验。在商业领域，营销者会珍视以尽可能多的情感和谐、最少干扰和最吸引人的方式投放广告的机会。这项技术将为营销人员提供通过算法观看数百万个视频的能力，从而实现比以往任何时候在电视或互联网上都更流畅和定制的观众体验。这个小型企业创新研究第一阶段项目寻求开发感知注释的商业应用，这是一项由NSF资助开发的技术，允许将人类表现的详细测量注入到机器学习过程中，使机器学习者既能表现得更好，又能以更符合人类的方式表现。通过将这种新的人类产生的监督信号添加到机器学习过程中，提出者证明了显著提高机器视觉性能是可能的，允许机器更好地概括到以前未见过的新图像。虽然该公司的技术已经在大规模的“野外”学术数据集上得到了严格的验证，但在拟议的SBIR第一阶段活动中，一个主要的技术驱动因素将是将公司的努力转移到对在线视频等“实时”、巨大且不断扩大的数据集的分析上。拟议的第一阶段工作的第二个主要驱动力将是构建“第二阶段”机器学习模型，该模型将基于感知注释的机器评级作为输入和输出可操作的营销决策。