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This paper presents a method for action recognition of individuals on airport aprons using thermal infrared images, addressing the need for improved surveillance in low-visibility conditions. The authors construct a new dataset, the Infrared Individual Apron Action (IIAA) dataset, and propose a classification network utilizing long short-term memory layers to achieve an accuracy of 87.2%. The study highlights the advantages of vision-based systems over traditional methods and aims to enhance airport ground

Author

Furqon Muhammad

Language

EN