SBIR Phase I: Visitor Demographic Analysis in Shopping Malls using Computer Vision

SBIR 第一阶段：使用计算机视觉进行购物中心的访客人口统计分析

• 批准号: 1938365
• 负责人: Zohar Kapach
• 金额: $ 21.99万
• 依托单位: OPSUS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938365&HistoricalAwards=false
• 关键词: SBIR; Phase; Visitor; Demographic; Analysis

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from building a computer vision platform that will help make communities more useful and appealing to their occupants regardless of demographic data, with shopping malls as the pilot application. Shopping malls of the future will blur the lines between community center and retail space as malls become mixed-use spaces where people will not only shop, but will also live, work, and play. To make this transition, shopping mall companies need detailed demographic information regarding mall visitors. The company’s technology will enable improved state-of-the-art pedestrian movement and demographic classification, without collecting any personally identifiable information. This will enable shopping mall operators to understand and improve their tenant mix, and thereby better foster communities nationwide. The data generated could also benefit other industries by revolutionizing property analysis: governments can use demographic data to analyze changing populations and urban planners can use foot-traffic information to make cities cleaner and more efficient. The technology will enable a more detailed understanding of changing communities and how to help them thrive, without the privacy compromises that usually accompany video-based technologies. The company intends to remain at the forefront of privacy-preserving practices with regard to data collection and management.This Small Business Innovation Research (SBIR) Phase I project will solve three crucial problems in video analytics for obtaining demographic information, while avoiding the need to collect personally identifiable information, namely: (1) classification of pedestrian demographics in a public setting, (2) accurate group detection and path (motion) analysis, and (3) stable path analysis using network camera imagery. There are currently no large-scale systems capable of accurately extracting both demographic and path information from network camera imagery. The proposed technology will analyze video data from a network of cameras placed throughout a shopping mall, extract demographic foot-traffic patterns to generate demographic and path data, and then permanently delete the video. Developing technology that effectively recovers from occlusions that arise in dense environments is crucial to the commercial adoption of the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将来自于建立一个计算机视觉平台，该平台将有助于使社区更有用，无论人口统计数据如何，对居住者都更具吸引力，购物中心作为试点应用。 未来的购物中心将模糊社区中心和零售空间之间的界限，因为购物中心将成为混合用途的空间，人们不仅可以购物，还可以生活，工作和娱乐。为了实现这一转变，购物中心公司需要有关购物中心访客的详细人口统计信息。该公司的技术将能够改进最先进的行人移动和人口分类，而无需收集任何个人身份信息。这将使购物中心运营商能够了解和改善其租户组合，从而更好地促进全国社区的发展。产生的数据还可以通过革命性的房地产分析使其他行业受益：政府可以使用人口统计数据来分析不断变化的人口，城市规划者可以使用步行交通信息来使城市更清洁，更高效。该技术将使人们能够更详细地了解不断变化的社区以及如何帮助他们蓬勃发展，而不会像基于视频的技术那样损害隐私。该公司打算在数据收集和管理方面保持在隐私保护实践的最前沿。这个小型企业创新研究（SBIR）第一阶段项目将解决视频分析中的三个关键问题，以获得人口统计信息，同时避免收集个人身份信息，即：（1）公共环境中行人人口统计的分类，（2）精确的组检测和路径（运动）分析，以及（3）使用网络摄像机图像的稳定路径分析。目前还没有能够从网络摄像机图像中准确提取人口统计和路径信息的大规模系统。这项技术将分析来自整个购物中心的摄像头网络的视频数据，提取人口统计学的步行模式以生成人口统计学和路径数据，然后永久删除视频。开发能够有效地从密集环境中出现的闭塞中恢复的技术对于该项目的商业应用至关重要。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。