论文标题
Slitranet：使用卷积神经网络自动检测演讲视频中的幻灯片过渡
SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks
论文作者
论文摘要
随着网络中的在线学习材料数量的增加，在讲座视频中搜索特定内容可能很耗时。因此，从讲座视频中自动幻灯片提取可能会有所帮助，简要概述主要内容并支持学生学习。对于此任务，我们提出了一种深度学习方法，以检测讲座视频中的幻灯片过渡。我们首先使用2D卷积神经网络来预测过渡候选者，首先通过基于启发式的方法来处理视频的每个帧。然后，我们通过使用两个3D卷积神经网络来完善过渡候选者来增加复杂性。评估结果证明了我们方法在寻找幻灯片过渡方面的有效性。
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.
