Construction and application of aircraft turnaround time research based on the video analysis platform

Xiang Jin; Jianjun Peng; Xingyun Jiao; Chengshuang Hu

doi:10.1117/12.3014475

9 January 2024 Construction and application of aircraft turnaround time research based on the video analysis platform

Xiang Jin, Jianjun Peng, Xingyun Jiao, Chengshuang Hu

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129691Q (2024) https://doi.org/10.1117/12.3014475
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

Airport Collaborative Decision Making (A-CDM) is a system designed to improve the overall operational efficiency of airports. Flight support node information serves as a critical data source for the A-CDM system. Traditional manual methods of collecting node information pose several challenges, including issues related to timeliness and accuracy, making it difficult to meet the system's requirements. Some ground support operation data cannot be automatically obtained. To address this issue, this paper introduces the design concept of A-CDM time node collection using a video analysis service platform. It outlines the key technologies, components, and the implementation process of the algorithm repository, as well as discusses its future prospects. This solution aims to automatically collect data, such as milestone event timestamps during flight airport turnarounds, which are difficult to obtain manually. It brings economic and social benefits to airports and lays the foundation for smart airport development.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiang Jin, Jianjun Peng, Xingyun Jiao, and Chengshuang Hu "Construction and application of aircraft turnaround time research based on the video analysis platform", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129691Q (9 January 2024); https://doi.org/10.1117/12.3014475

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