This paper describes the authors’ efforts to develop a system that could collect shoe scans of a populace in real-time as they walk over the scanner without committing any privacy violations; the goal was to identify specific traits on the soles of the shoes to determine habits of the wearers as well as information about the shoes themselves; it also highlights project results, including the development of the MANTIS system.
There currently are no shoe-scanning devices developed in the United States that can operate in a real-world, variable-weather environment in real-time. Forensics-focused groups, including the NIJ, expressed the need for such a system. To accomplish this, there was first a need to collect a large amount of sample data to train a first-of-its-kind machine-learning model. The work aimed to develop a system to collect shoe scans of a populace in real-time as they walk without committing privacy violations. This system would be able to identify specific traits found on the soles of the shoes, enabling insight into the origins of the shoes, where and how they are worn, as well as the people who wear them. The primary question of this research is if the authors could develop a prototype of such a system and what challenges would have to be overcome to make this possible. (Published Abstract Provided)
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