# Improved Soccer Action Spotting using both Audio and Video Streams Source: [Improved Soccer Action Spotting using both Audio and Video Streams]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:23:06.064Z Authors: Bastien Vanderplaetse, St\\'ephane Dupont Published: 2020-11-09T09:12:44.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Action Classification on SoccerNet (test)](): Action Classification; SoccerNet (test); mAP: 75.2. - [Action spotting on SoccerNet v1 (test)](): Action spotting; SoccerNet v1 (test); Average-mAP: not recorded. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()