Improved Soccer Action Spotting Using Both Audio and Video Streams
Abstract
In this paper, we propose a study on multi-modal (audio and video) action spotting and classification in soccer videos. Action spotting and classification are the tasks that consist in finding the temporal anchors of events in a video and determine which event they are. This is an important application of general activity understanding. Here, we propose an experimental study on combining audio and video information at different stages of deep neural network architectures. We used the SoccerNet benchmark dataset, which contains annotated events for 500 soccer game videos from the Big Five European leagues. Through this work, we evaluated several ways to integrate audio stream into video-only-based architectures. We observed an average absolute improvement of the mean Average Precision (mAP) metric of 7.43% for the action classification task and of 4.19% for the action spotting task.
Cite
Text
Vanderplaetse and Dupont. "Improved Soccer Action Spotting Using Both Audio and Video Streams." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00456Markdown
[Vanderplaetse and Dupont. "Improved Soccer Action Spotting Using Both Audio and Video Streams." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/vanderplaetse2020cvprw-improved/) doi:10.1109/CVPRW50498.2020.00456BibTeX
@inproceedings{vanderplaetse2020cvprw-improved,
title = {{Improved Soccer Action Spotting Using Both Audio and Video Streams}},
author = {Vanderplaetse, Bastien and Dupont, Stéphane},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {3921-3931},
doi = {10.1109/CVPRW50498.2020.00456},
url = {https://mlanthology.org/cvprw/2020/vanderplaetse2020cvprw-improved/}
}