Design and implementation of a gesture recognition self-service platform for shopping mall based on data augmentation and deep learning
Nowadays,the continuous advancement of economic globalization has brought development opportunities to the world,resulting in a boom in trade and human mobility,and improving the lives of billions of people around the world.However,at the same time,pathogens are easier to spread across different regions,thereby increasing the speed and scope of infectious diseases and raising the risk of transmission.Under such circumstances,self-service machines undoubtedly serve as a good alternative to manual labor.However,users still need to have contact when operating traditional self-service machines,which undoubtedly brings certain health risks.Therefore,it is of great significance to study how to accurately operate the machines without touching their sur-faces.Based on the above problems,the entity relationship model of self-service machines in shopping malls is deeply studied,and a self-service system in shopping malls that can be operated by gestures is designed,and various data augmentation methods in ges-ture recognition scenarios are studied and designed to improve the accuracy of gesture recognition.
data augmentationsample classificationgesture recognitionnon-contactsystem design
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