Biblio

Automatic algorithms for tracking and associating passengers and their divested objects at an airport security screening checkpoint would have great potential for improving checkpoint efficiency, including flow analysis, theft detection, line-of-sight maintenance, and risk-based screening. In this paper, we present algorithms for these tracking and association problems and demonstrate their effectiveness in a full-scale physical simulation of an airport security screening checkpoint. Our algorithms leverage both hand-crafted and deep-learning-based approaches for passenger and bin tracking, and are able to accurately track and associate objects through a ceiling-mounted multicamera array. We validate our algorithm on ground-truthed datasets collected at the simulated checkpoint that reflect natural passenger behavior, achieving high rates of passenger/object/transfer event detection while maintaining low false alarm and mismatch rates.