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README.md

KidsGuard: Fine-Grained Approach for Child Unsafe Video Representation and Detection

This repository contains the code for the implementation of the paper titled KidsGuard: Fine-Grained Approach for Child Unsafe Video Representation and Detection by Singh et al. published at ACM SAC 2019.

Dataset

The dataset used for the paper can be from here.

Experiment Steps

  • Start by downloading the dataset.
  • Download the YouTube videos using the video IDs mentioned in the dataset
  • Once downloaded, use the notebooks in directory /extract-video to obtain video frames and then their VGG16 features.
  • Use the notebooks in the /process directory to parse annotations from the downloaded dataset, and aggregate clips and features for experiments.
  • The notebooks in /train directory contain the notebooks to train the autoencoder and the classifier.
  • /metrics contains the notebook to plot the training and testing results.

Dependencies

The project uses Python 3 dependencies explicitly, for processing and training. All the code is run on JupyterLab computational environment and Anaconda is used as a package manager as well as a virtual environment manager. All the dependencies are exported in the environment.yml file. Make a new environment using:

$ conda env create -f environment.yml

Citation

If you found this code or our paper useful, please consider citing the following paper: