Over the past few years, there has been significant interest in video action recognition systems and models. However, direct comparison of accuracy and computational performance results remain clouded by differing training environments, hardware specifications, hyperparameters, pipelines, and inference methods. This article provides a direct comparison between fourteen off-the-shelf and state-of-the-art models by ensuring consistency in these training characteristics in order to provide readers with a meaningful comparison across different types of video action recognition algorithms. Accuracy of the models is evaluated using standard Top-1 and Top-5 accuracy metrics in addition to a proposed new accuracy metric. Additionally, we compare computational performance of distributed training from two to sixty-four GPUs on a state-of-the-art HPC system.
@article{arxiv.2008.09037,
title = {Accuracy and Performance Comparison of Video Action Recognition Approaches},
author = {Matthew Hutchinson and Siddharth Samsi and William Arcand and David Bestor and Bill Bergeron and Chansup Byun and Micheal Houle and Matthew Hubbell and Micheal Jones and Jeremy Kepner and Andrew Kirby and Peter Michaleas and Lauren Milechin and Julie Mullen and Andrew Prout and Antonio Rosa and Albert Reuther and Charles Yee and Vijay Gadepally},
journal= {arXiv preprint arXiv:2008.09037},
year = {2021}
}