SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks
This addresses the time-consuming search for specific content in online lecture videos, though it appears incremental as it builds on existing CNN approaches for video analysis.
The paper tackled the problem of automatically detecting slide transitions in lecture videos to aid content search and student learning, proposing a deep learning method that uses 2-D and 3-D convolutional neural networks, with evaluation results showing its effectiveness.
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.