SlowFast with DropBlock and smooth samples loss for student action recognition

Chuanming Li; Wenxing Bao; Xu Chen; Yongjun Jing; Xiudong Qu

doi:10.1117/12.2644370

12 October 2022 SlowFast with DropBlock and smooth samples loss for student action recognition

Chuanming Li, Wenxing Bao, Xu Chen, Yongjun Jing, Xiudong Qu

Author Affiliations +

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123420P (2022) https://doi.org/10.1117/12.2644370
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

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.

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Chuanming Li, Wenxing Bao, Xu Chen, Yongjun Jing, and Xiudong Qu "SlowFast with DropBlock and smooth samples loss for student action recognition", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123420P (12 October 2022); https://doi.org/10.1117/12.2644370

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KEYWORDS

Video

Convolution

Data modeling

Video surveillance

RGB color model

Convolutional neural networks

Image processing

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