133186Low cost baggage tracking, through machine learning and image processing.ClosedFeasibility StudiesInnovate UKThe development of an innovative bag tag recognition system that uses the improved capabilities of low cost cameras and advances in image analysis and machine learning to individually identify bags as they are handled on their journey from check-in through to delivery to the passenger at their final destination. Mishandled baggage cost the air transport industry $2.3billion per annum, while fraudulent and exaggerated claims are becoming a growing problem. In an attempt to reduce mishandling and fraud, IATA, the trade association representing 265 airlines, has passed a resolution (753) that requires, by June 2018, that their members track checked-in baggage at key exchange points in the baggage handling process (check-in, loading, transfer and delivery). However, as of today, very few airlines and airports worldwide have the necessary infrastructure to meet these tracking obligations, primarily due to cost, lack of accuracy and the physical constraints of a typical baggage hall. A bag tag recognition system, built around compact, low cost cameras, advanced image analysis and machine learning should be able to address these legacy restrictions and provide airlines and airports with a credible and affordable option for more extensive baggage tracking.