Behavior Recognition and Exhibition Tour Scene Classification Using In-Depth Learning

Abstract

The rapid development of exhibition tourism has led to a sharp increase in the amount of data in the tourism and exhibition industry. Through in-depth mining and application of exhibition tourism data, it can intuitively show its potential relevance and produce much valuable knowledge. The huge value of exhibition tourism data can better meet social needs. Through the analysis of exhibition tourism data, it has certain use-value and significance for the development of the industry. Scene classification in the field of computer vision is a research hotspot. However, there are far few research-related algorithms on mice tourism scene classification. Therefore, based on in-depth learning, this paper studies behavior recognition, and mice tourism scene classification, applies computer vision technology to mice tourism scene classification, collects many visual data, and speeds up the rapid development of the field of vision. In this paper, the scene classification algorithm based on a self-attention generation countermeasure network is constructed to deal with the problem of convention and exhibition tourism. The test results show that the accuracy of the classification results of this algorithm is as high as 99.12%, which is the highest compared with the classification results of other models, which fully proves that this algorithm can accurately classify conventional and exhibition tourism scenes.