Details

Autor(en) / Beteiligte

• Liu, Zhijin
• Li, Kai
• Shen, Liquan
• Ma, Ran
• An, Ping

Titel

Spatio-Temporal Residual Networks for Slide Transition Detection in Lecture Videos

Ist Teil von

• KSII Transactions on Internet and Information Systems, 2019, 13(8), , pp.4026-4040

Ort / Verlag

KSII, the Korean Society for Internet Information

Erscheinungsjahr

2019

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• In this paper, we present an approach for detecting slide transitions in lecture videos by introducing the spatio-temporal residual networks. Given a lecture video which records the digital slides, the speaker, and the audience by multiple cameras, our goal is to find keyframes where slide content changes. Since temporal dependency among video frames is important for detecting slide changes, 3D Convolutional Networks has been regarded as an efficient approach to learn the spatio-temporal features in videos. However, 3D ConvNet will cost much training time and need lots of memory. Hence, we utilize ResNet to ease the training of network, which is easy to optimize. Consequently, we present a novel ConvNet architecture based on 3D ConvNet and ResNet for slide transition detection in lecture videos. Experimental results show that the proposed novel ConvNet architecture achieves the better accuracy than other slide progression detection approaches. Keywords: Lecture video, slide transition, 3D ConvNet, ResNet