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.
