Multi-scale Mobile Phone Playing Behavior Recognition Based on Temporal Enhancement and Interaction

Analyzing students' classroom behavior is an important part of evaluating classroom teaching effects in the education field, and the behavior of using mobile phones is an important manifestation of students' learning status. Therefore, the status of using mobile phones in the classroom can reflect the effect of classroom teaching to a certain extent. This article establishes a video-based classroom student behavior data set, and divides the behavior categories in the data into two categories: mobile phone playing and other; analysis of students playing mobile phone and other behaviors reveals that there are subtle movements in student behavior and a certain visual tempo. The resolution is low, and the occlusion is serious. In response to the above problems, this paper proposes a multi-scale mobile phone playing behavior recognition method based on temporal information enhancement and interaction. First, use the motion enhancement module to enhance the motion information between two frames to improve the recognition ability of subtle actions; secondly, add the temporal pyramid to extract the multi-scale features of the action, and then obtain the visual tempo information of the video; finally add the temporal information interaction module to enhance the temporal dimension information interaction , To further model the temporal information. The experimental results on the self-made student action dataset StudentAction show that compared with the existing methods, the algorithm has significantly improved recognition accuracy and better solves the problem of low accuracy in the recognition of subtle actions. Good performance have shown on the public datasets HMDB51 and UCF101, indicating that the method has strong generalization ability and can adapt to the recognition problems of different scene actions.