Related papers: Improved Soccer Action Spotting using both Audio and Video Streams

Improved Soccer Action Spotting using both Audio and Video Streams

URL: http://arxiv.org/abs/2011.04258v1
Date: Mon, 9 Nov 2020 09:12:44 GMT
Title: Improved Soccer Action Spotting using both Audio and Video Streams
Authors: Bastien Vanderplaetse, St\'ephane Dupont
Abstract summary: We propose a 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. 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.
Score: 3.4376560669160394
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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