Video understanding has received more attention in the past few years due to the availability of several large-scale video datasets. However, annotating large-scale video datasets are cost-intensive. In this work, we propose a time-efficient video annotation method using spatio-temporal feature similarity and t-SNE dimensionality reduction to speed up the annotation process massively. Placing the same actions from different videos near each other in the two-dimensional space based on feature similarity helps the annotator to group-label video clips. We evaluate our method on two subsets of the ActivityNet (v1.3) and a subset of the Sports-1M dataset. We show that t-EVA can outperform other video annotation tools while maintaining test accuracy on video classification.
@article{arxiv.2011.13202,
title = {t-EVA: Time-Efficient t-SNE Video Annotation},
author = {Soroosh Poorgholi and Osman Semih Kayhan and Jan C. van Gemert},
journal= {arXiv preprint arXiv:2011.13202},
year = {2020}
}