SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries

Mkhallati, Hassan; Cioppa, Anthony; Giancola, Silvio; Ghanem, Bernard; Droogenbroeck, Marc Van

doi:10.1109/cvprw59228.2023.00536

Cited by 16 publications

(1 citation statement)

References 67 publications

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“…Automated sport analysis involves the use of computer vision and deep learning techniques to analyze sports events and extract meaningful insights. It shows potential applications in a variety of areas, such as sports broadcasting [9,43,48], autonomous and personalized production [5,7,31], coaching [1,6,17,42], and performance analysis [11,20,40,41,44,54]. One of the key components of automated sport analysis is object tracking, which involves the identification of players and objects all along a video.…”

Section: Introductionmentioning

confidence: 99%

Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

Mansourian,

Somers,

De Vleeschouwer

et al. 2023

Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports

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Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the nonlinear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Therefore, the ability to extract meaningful embeddings to represent players is crucial in developing an effective tracking and re-identification system. In this paper, a multi-purpose part-based person representation method, called PRTreID, is proposed that performs three tasks of role classification, team affiliation, and re-identification, simultaneously. In contrast to available literature, a single network is trained with multi-task supervision to solve all three tasks, jointly. The proposed joint method is computationally efficient due to the shared backbone. Also, the multi-task learning leads to richer and more discriminative representations, as demonstrated by both quantitative and qualitative results. To demonstrate the effectiveness of PRTreID, it is integrated with a state-of-the-art tracking method, using a part-based post-processing module to handle long-term tracking. The proposed tracking method, outperforms all existing tracking methods on the challenging SoccerNet tracking dataset. CCS CONCEPTS• Computing methodologies → Tracking.

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Section: Introductionmentioning

confidence: 99%

Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

Mansourian,

Somers,

De Vleeschouwer

et al. 2023

Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports

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A survey of video-based human action recognition in team sports

Yin,

Sinnott,

Jayaputera

2024

Artif Intell Rev

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Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex actions involving multiple people interacting with objects. Actions in team sports exhibit a different nature compared to other sports, since they tend to occur at a faster pace and involve more human-human interactions. As a result, research has typically not focused on the challenges of HAR in team sports. This paper comprehensively summarises HAR-related research and applications with specific focus on team sports such as football (soccer), basketball and Australian rules football. Key datasets used for HAR-related team sports research are explored. Finally, common challenges and future work are discussed, and possible research directions identified.

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