Kaggle Competitions

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Passenger Screening Algorithm Challenge

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• Passenger Screening Algorithm Challenge
  • 1st Place | idle_speculation
  • 10th Place | MoeJoe (Shayan)
  • Muneeb Saleem

While long lines and frantically shuffling luggage into plastic bins isn’t a fun experience, airport security is a critical and necessary requirement for safe travel. No one understands the need for both thorough security screenings and short wait times more than U.S. Transportation Security Administration (TSA). They’re responsible for all U.S. airport security, screening more than two million passengers daily. As part of their Apex Screening at Speed Program, DHS has identified high false alarm rates as creating significant bottlenecks at the airport checkpoints. Whenever TSA’s sensors and algorithms predict a potential threat, TSA staff needs to engage in a secondary, manual screening process that slows everything down. And as the number of travelers increase every year and new threats develop, their prediction algorithms need to continually improve to meet the increased demand. Currently, TSA purchases updated algorithms exclusively from the manufacturers of the scanning equipment used. These algorithms are proprietary, expensive, and often released in long cycles. In this competition, TSA is stepping outside their established procurement process and is challenging the broader data science community to help improve the accuracy of their threat prediction algorithms. Using a dataset of images collected on the latest generation of scanners, participants are challenged to identify the presence of simulated threats under a variety of object types, clothing types, and body types. Even a modest decrease in false alarms will help TSA significantly improve the passenger experience while maintaining high levels of security.

• Multi-view CNN (MVCNN) for shape recognition
• LightGBM - gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
• ImageNet | Wikipedia