Aims to develop re-identification methods that autonomously associate objects across diverse, non-collaborative, video sensor footage, to distill raw pixel data into spatiotemporal motion vectors, providing the ability to analyze these patterns for anomalies and threats. This includes the creation of models of “normal” human movement across times, locations, and people in order to characterize what makes an activity detectable as anomalous within the expanding corpus of global human trajectory data.
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Job Type
Full-time
Career Level
Senior