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DA-ResNet: dual-stream ResNet with attention mechanism for classroom video summary

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Abstract

It is important to generate both diverse and representative video summary for massive videos. In this paper, a convolution neural network based on dual-stream attention mechanism(DA-ResNet) is designed to obtain candidate summary sequences for classroom scenes. DA-ResNet constructs a dual stream input of image frame sequence and optical flow frame sequence to enhance the expression ability. The network also embeds the attention mechanism into ResNet. On the other hand, the final video summary is obtained by removing redundant frames with the improved hash clustering algorithm. In this process, preprocessing is performed first to reduce computational complexity. And then hash clustering is used to retain the frame with the highest entropy value in each class, removing other similar frames. To verify its effectiveness in classroom scenes, we also created ClassVideo, a real dataset consisting of 45 videos from the normal teaching environment of our school. The results of the experiments show the competitiveness of the proposed method DA-ResNet outperforms the existing methods by about 8% in terms of the F-measure. Besides, the visual results also demonstrate its ability to produce classroom video summaries that are very close to the human preferences.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Haigang, Qinhuangdao, 066004, Hebei, China

    Yuxiang Wu, Xiaoyan Wang, Tianpan Chen & Yan Dou

  2. Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Haigang, Qinhuangdao, 066004, Hebei, China

    Xiaoyan Wang & Yan Dou

Authors
  1. Yuxiang Wu
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Yuxiang Wu and Tianpan Chen are primarily accountable for experimental implementation and writing the full-text manuscript. Xiaoyan Wang and Yan Dou are mainly responsible for the architectural design and content review of the full-text manuscript.

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Correspondence to Xiaoyan Wang.

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Wu, Y., Wang, X., Chen, T. et al. DA-ResNet: dual-stream ResNet with attention mechanism for classroom video summary. Pattern Anal Applic 27, 32 (2024). https://doi.org/10.1007/s10044-024-01256-1

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