SlowFast with DropBlock and smooth samples loss for student action recognition

Abstract

Due to the advent of large-scale video datasets, action recognition using three-dimensional convolutions (3D CNNs) containing spatiotemporal information has become mainstream. Aiming at the problem of classroom student behavior recognition, the paper adopts the improved SlowFast network structure to deal with spatial structure and temporal events respectively. First, DropBlock (a regularization method) is added to the SlowFast network to solve the overfitting problem. Second, for the problem of Long-Tailed Distribution, the designed Smooth Sample (SS) Loss function is added to the network to smooth the number of samples. Classification experiments show that compared with similar methods, the model accuracy of our method on the Kinetics and Student Action Dataset is increased by 2.1% and 2.9%, respectively.