SliTraNet

Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks

This is the source code to the conference article "SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks" published at OAGM Workshop 2021.

If you use the code, please cite our paper (arxiv)

  @InProceedings{sindel2022slitranet,
	title={SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks},
	author={Aline Sindel and Abner Hernandez and Seung Hee Yang and Vincent Christlein and Andreas Maier},
	year={2022},
	booktitle={Proceedings of the OAGM Workshop 2021},
	doi={10.3217/978-3-85125-869-1-10},
	pages={59-64}		
  }

Requirements

Install the requirements using pip or conda (python 3):

• torch >= 1.7
• torchvision
• opencv-contrib-python-headless
• numpy
• decord

Usage

Data

The dataset needs to be in the following folder structure:

• Video files in: "/videos/PHASE/", where PHASE is "train", "val" or "test".
• Bounding box labels in: "/videos/PHASE_bounding_box_list.txt"

Bounding box labels define the rectangle of the slide area in the format: Videoname,x0,y0,x1,y1

Here one example test_bounding_box_list.txt file (the header needs to be included):
Video,x0,y0,x1,y1
Architectures_1,38,57,1306,1008
Architectures_2,38,57,1306,1008

Pretrained weights

The pretrained weights of SliTraNet from the paper can be downloaded here. Move them into the folder: "/weights"

SliTraNet Inference:

Run test_SliTraNet.py

Some settings have to be specified, as described in the python file, such as the dataset and output folders and model paths.

Stage 1 of SliTraNet can also be applied separately (see test_slide_detection_2d.py) and afterwards the results can be loaded in test_SliTraNet.py.

@author Aline Sindel