On the Performance Evaluation of Action Recognition Models on Transcoded Low Quality Videos

Abstract

In the design of action recognition models, the quality of videos in the dataset is an important issue, however the trade-off between the quality and performance is often ignored. In general, action recognition models are trained and tested on high-quality videos, but in actual situations where action recognition models are deployed, sometimes it might not be assumed that the input videos are of high quality. In this study, we report qualitative evaluations of action recognition models for the quality degradation associated with transcoding by JPEG and H.264/AVC. Experimental results are shown for evaluating the performance of pre-trained models on the transcoded validation videos of Kinetics400. The models are also trained on the transcoded training videos. From these results, we quantitatively show the degree of degradation of the model performance with respect to the degradation of the video quality.

Why is the video analytics accuracy fluctuating, and what can we do about it?

“…al. [35] show the impact of low video quality on analytics, but they study network variability rather than the camera being the reason for low video quality.…”