This report describes a research project that resulted in the development, testing, and deployment of the MANTIS scanner for capturing images of shoe soles and uppers as the scanner is traversed; the paper describes the research methodology, outcomes, and limitations, and notes activities and accomplishments stemming from the project.
This paper reports on a project that had an overall goal of enabling footwear examiners to make empirical statements about the frequency of class characteristics in the local geographic population. To that end, the author created the following three project objectives: to develop robust, weather-resistant scanning equipment that can passively gather images of shoe soles and uppers from local populations; to develop automated software to automatically identify relevant class characteristic information from images collected by the scanning equipment; and to collect local population footwear data over multiple seasons, weather conditions, days of the week, times of the day, and assess the changes in identified class characteristic frequency associated with temporal and weather-related variability. The author reports completing the first objective, and partially completing the second objective, but that the third objective has not yet been completed due to COVID-19 pandemic and review board restrictions.