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.
The dataset used for the paper can be from here.
- 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-videoto obtain video frames and then their VGG16 features.
- Use the notebooks in the
/processdirectory to parse annotations from the downloaded dataset, and aggregate clips and features for experiments.
- The notebooks in
/traindirectory contain the notebooks to train the autoencoder and the classifier.
/metricscontains the notebook to plot the training and testing results.
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
If you found this code or our paper useful, please consider citing the following paper: