Replay Grounding

SoccerNet Replay Grounding

Replay grounding consists in retrieving the timestamp of the action shown in a given replay shot within the whole game. Grounding a replay with its action confers it an estimation of importance, which is otherwise difficult to assess. Derived applications may be further built on top of this task, e.g. automatic highlight production, as the most replayed actions are usually the most relevant.

Our task.

Replay grounding consists in localizing when an action shown in a replay occurs in the complete broadcast game. Each replayed action is annotated with a single timestamp inside the broadcast video.

Our classes.

Unlike action spotting, replay grounding does not take the action class into account. This can be seen as having a single action class.

Our data.

The data consists of 500 videos from soccer broadcast games available at two resolutions (720p and 224p). We also provide extracted features at 2 frames per second for an easier use, including the feature used by the 2021 challenge winners, Baidu Research. The challenge set is composed of 50 separate games.

Our Metric.

Traditionally, the average-AP as defined in this original paper was used as the main metric. However, starting in 2022, we introduced the tight average-AP, which is the same metric, but with a smaller temporal tolerance ([1-5] seconds instead of [5-60 seconds]).

For more details, check out our development kit on github

Published research leaderboard

2022 challenge leaderboard

2021 challenge leaderboard

If your work is missing from one of these tables, don't hesitate to contact us through one of our social media.

Our videos on Replay Grounding

SoccerNet-v2: a new MASSIVE soccer dataset

In this video, we present our paper: “SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos” published at the CVPR 2021 workshop CVsports. We provide 300,000 temporal annotations within 500 soccer games. This allows a 17-class action spotting task, a 13-class camera boundary detection task, and a novel replay grounding task. We provide benchmarks for all these tasks to start an international challenge.

SoccerNet-v2 challenge - Tutorial #5 part 2 (live session) ft. Meisam J. Seikavandi

Second part of our fifth live tutorial presenting our challenge: SoccerNet-v2! In this tutorial, our colleague Meisam presents our replay grounding baseline, with some code demos.

How to cite this work ?


title={SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos},

author={Adrien Deliège and Anthony Cioppa and Silvio Giancola and Meisam J. Seikavandi and Jacob V. Dueholm and Kamal Nasrollahi and Bernard Ghanem and Thomas B. Moeslund and Marc Van Droogenbroeck},


booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},

month = {June},