Attention based fully convolutional network for video summarization

Cong Li; Shuangxiong Wei; Yuxuan Liu; Siyi Luo; Di Yang; Zengkai Wang

doi:10.1117/12.2636379

6 May 2022 Attention based fully convolutional network for video summarization

Cong Li, Shuangxiong Wei, Yuxuan Liu, Siyi Luo, Di Yang, Zengkai Wang

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121761Q (2022) https://doi.org/10.1117/12.2636379
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Video summarization technology extracts key frames or video clips that can most effectively express video content from the original long video, so that the summarized video clips contain the content information that users are most interested in, which is convenient for users to quickly browse and retrieve the video. In this paper, we regard video summarization as a sequence annotation problem. Different from the existing methods using recurrent models, this paper proposes a fully convolutional neural network model combining spatial attention mechanism to solve the video summarization problem. Firstly, the pre-training model is used to extract the cube features of the input video frame, then the cube features are aggregated into the attention vector by the attention mechanism, which is input into the fully convolutional neural network model for binary classification, and then the video summary is generated. Extensive experiments and analysis on two benchmark datasets demonstrate the effectiveness of the proposed method.

Citation Download Citation

Cong Li, Shuangxiong Wei, Yuxuan Liu, Siyi Luo, Di Yang, and Zengkai Wang "Attention based fully convolutional network for video summarization", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121761Q (6 May 2022); https://doi.org/10.1117/12.2636379

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KEYWORDS

Video

Video surveillance

Convolution

Semantic video

Image segmentation

Binary data

Convolutional neural networks

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