3 research outputs found
SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries
Soccer is more than just a game - it is a passion that transcends borders and
unites people worldwide. From the roar of the crowds to the excitement of the
commentators, every moment of a soccer match is a thrill. Yet, with so many
games happening simultaneously, fans cannot watch them all live. Notifications
for main actions can help, but lack the engagement of live commentary, leaving
fans feeling disconnected. To fulfill this need, we propose in this paper a
novel task of dense video captioning focusing on the generation of textual
commentaries anchored with single timestamps. To support this task, we
additionally present a challenging dataset consisting of almost 37k timestamped
commentaries across 715.9 hours of soccer broadcast videos. Additionally, we
propose a first benchmark and baseline for this task, highlighting the
difficulty of temporally anchoring commentaries yet showing the capacity to
generate meaningful commentaries. By providing broadcasters with a tool to
summarize the content of their video with the same level of engagement as a
live game, our method could help satisfy the needs of the numerous fans who
follow their team but cannot necessarily watch the live game. We believe our
method has the potential to enhance the accessibility and understanding of
soccer content for a wider audience, bringing the excitement of the game to
more people
SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries
Soccer is more than just a game - it is a passion that transcends borders and
unites people worldwide. From the roar of the crowds to the excitement of the
commentators, every moment of a soccer match is a thrill. Yet, with so many
games happening simultaneously, fans cannot watch them all live. Notifications
for main actions can help, but lack the engagement of live commentary, leaving
fans feeling disconnected. To fulfill this need, we propose in this paper a
novel task of dense video captioning focusing on the generation of textual
commentaries anchored with single timestamps. To support this task, we
additionally present a challenging dataset consisting of almost 37k timestamped
commentaries across 715.9 hours of soccer broadcast videos. Additionally, we
propose a first benchmark and baseline for this task, highlighting the
difficulty of temporally anchoring commentaries yet showing the capacity to
generate meaningful commentaries. By providing broadcasters with a tool to
summarize the content of their video with the same level of engagement as a
live game, our method could help satisfy the needs of the numerous fans who
follow their team but cannot necessarily watch the live game. We believe our
method has the potential to enhance the accessibility and understanding of
soccer content for a wider audience, bringing the excitement of the game to
more people
SoccerNet: Exploring the Game with Computer Vision
SoccerNet is a large-scale video dataset for video undestanding in soccer. The dataset has been used in various research challenges, where participants competed to develop the best algorithms for tasks such as predicting events, tracking players or recognising their jersey number. The challenges have spurred advancements in the state-of-the-art in sports video analysis