Sindel A, Hernandez A, Yang SH, Christlein V, Maier A (2022)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2022
Publisher: Verlag der Technischen Universität Graz
Conference Proceedings Title: Proceedings of the OAGM Workshop 2021. Computer Vision and Pattern Analysis Across Domains
ISBN: 978-3-85125-869-1
URI: https://openlib.tugraz.at/download.php?id=621f329186973&location=browse
DOI: 10.3217/978-3-85125-869-1-10
With the increasing number of online learning material in the web, search for specific content in lecture videos can be time consuming. Therefore, automatic slide extraction from the lecture videos can be helpful to give a brief overview of the main content and to support the students in their studies. For this task, we propose a deep learning method to detect slide transitions in lectures videos. We first process each frame of the video by a heuristic-based approach using a 2-D convolutional neural network to predict transition candidates. Then, we increase the complexity by employing two 3-D convolutional neural networks to refine the transition candidates. Evaluation results demonstrate the effectiveness of our method in finding slide transitions.
APA:
Sindel, A., Hernandez, A., Yang, S.H., Christlein, V., & Maier, A. (2022). SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks. In Proceedings of the OAGM Workshop 2021. Computer Vision and Pattern Analysis Across Domains. Verlag der Technischen Universität Graz.
MLA:
Sindel, Aline, et al. "SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks." Proceedings of the OAGM Workshop 2021 Verlag der Technischen Universität Graz, 2022.
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