Low cost baggage tracking, through machine learning and image processing.

通过机器学习和图像处理实现低成本行李跟踪。

• 批准号: 133186
• 金额: $ 38.53万
• 依托单位: LUGGAGE LOGISTICS LTD
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=133186
• 关键词: Low; cost; baggage; tracking; through

The 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.

开发一种创新的行李标签识别系统，该系统利用低成本摄像头改进的功能以及图像分析和机器学习的先进技术，在行李从办理登机手续到运送到最终目的地的过程中单独识别行李。行李处理不当每年给航空运输业造成23亿美元的损失，而欺诈性和夸大的索赔正在成为一个日益严重的问题。为了减少不当处理和欺诈行为，代表265家航空公司的行业协会国际航空运输协会(IATA)通过了一项决议(753)，要求其成员在2018年6月之前在行李处理过程(托运、装载、转运和交付)的关键交换点跟踪托运行李。然而，到今天为止，世界上很少有航空公司和机场拥有必要的基础设施来履行这些跟踪义务，这主要是由于成本、缺乏准确性以及典型行李大厅的物理限制。围绕紧凑型低成本摄像头、先进的图像分析和机器学习构建的行李标签识别系统应该能够解决这些遗留限制，并为航空公司和机场提供可信且负担得起的选择，以进行更广泛的行李跟踪。