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Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this requires a fully automated, robust end-to-end pipeline: from visual input, to player and group activities, to player and team ...

Effective analysis of skills requires high-quality, multi-modal datasets, especially in the field of artificial intelligence. However, creating such datasets for extreme sports, such as alpine skiing, can be challenging due to environmental constraints. ...

Running as one of the most popular sports comes with many positive effects, but also with risks. Most injuries are caused by overexertion. To optimise training and prevent injuries, approaches are needed to easily monitor training behaviour. Previous ...

Sports highlights are an important form of media for fans worldwide, as they provide short videos that capture key moments from games, often accompanied by the original commentaries of the game's announcers. However, traditional forms of presenting ...

Advanced data collection technologies, computational tools, and sophisticated algorithms have a revolutionary impact on sports analytics on various aspects of sports, from athletes performance to fan engagement. Computer Vision (CV) and Deep Learning (...

Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large ...

Automatic evaluating systems are fundamental issues in sports technologies. In many sports, such as figure skating, automated evaluating methods based on pose estimation have been proposed. However, previous studies have evaluated skaters' skills in 2D ...

Accurately localizing objects in three dimensions (3D) is crucial for various computer vision applications, such as robotics, autonomous driving, and augmented reality. This task finds another important application in sports analytics and, in this work, ...

Ball detection in sports, particularly in fast-paced games like volleyball, where the ball is constantly in high motion, presents a significant challenge for game analysis and automated sports broadcasting. Conventional camera-based ball detection faces ...

Using videos to analyze pitchers in baseball can play a vital role in strategizing and injury prevention. Computer vision-based pose analysis offers a time-eficient and cost-effective approach. However, the use of accessible broadcast videos, with a ...

Hockey rink registration is a useful tool for aiding and automating sports analysis. When combined with player tracking, it can provide location information of players on the rink by estimating a homography matrix that can warp broadcast video frames ...

In this study, we employ an extensive single-season dataset of event and positional data, as well as machine learning techniques, to build an Expected Goals (xG) model for handball. The selected features of the data include distances, angles, and game ...

In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the ...

Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the non-linear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. ...

The long-standing problem of novel view synthesis has many applications, notably in sports broadcasting. Photorealistic novel view synthesis of soccer actions, in particular, is of enormous interest to the broadcast industry. Yet only a few industrial ...

Player identification is a crucial component in vision-driven soccer analytics, enabling various downstream tasks such as player assess- ment, in-game analysis, and broadcast production. However, auto- matically detecting jersey numbers from player ...

The ACM MMSports2023 DeepSportRadar Basketball Player Instance Segmentation Challenge was focused on addressing the issue of occlusion. The dataset's primary characteristics include vast background areas, a high degree of occlusion between athletes, and ...

Instance segmentation is a fundamental computer vision task with widespread applications. Numerous novel methods have been proposed to address this task. However, limited data and occlusion are common issues that hinder the practical application of ...

Person Re-Identification (Re-ID) emerges as a important technique in sports analytics, enabling the accurate matching and recognition of players throughout a game. The fundamental objective of the Person Re-ID task is to identify the same player across ...

Cricket Bowl Release Detection aims to segment specific portions of bowl release actions occurring in multiple videos, with a focus on detecting the entire time window of this action. Unlike traditional detection tasks that identify action categories at ...

In recent years, there have been significant advancements in the field of temporal action detection. However, few studies have focused on detecting actions in sporting events. In this context, the MMSports 2023 cricket bowl release challenge aims to ...