PadelTracker100: Un conjunto de datos para el seguimiento inteligente de jugadores y pelotas en deportes de pádel

Advancements in computer vision and deep learning have revolutionized sports video analysis, enabling automated and precise data labeling. However, these technologies rely heavily on high-quality annotated datasets, which are essential for training supervised learning models. This article introduces PadelTracker100, a large-scale fully annotated dataset for padel, a rapidly growing sport, captured in a professional setting. The dataset, derived from two matches of the 2022 World Padel Tour (WPT) Finals at 1920 × 1080@30, contains nearly 100,000 frames with a single standard camera angle to reduce occlusions and ensure clarity. Annotations include ball trajectory tracking, real-world player positions, player pose estimation and shot event recognition, categorized into six classes: backhand, forehand, smash, serve, dropshot, and other. Ball trajectory annotations were generated using a semi-automatic pipeline with iterative YOLO training. Pose estimation was carried out with ViTPose-L, selected after comparing various state-of-the-art models. Shot events were annotated for 40,135 frames. A thorough manual refinement process was applied, ensuring annotation quality trough all annotation types. The lack of pre-annotated datasets has significantly restricted large-scale match analysis and the development of automated techniques in padel, hindering the progress of AI-driven solutions tailored for the sport. By addressing this gap, this dataset serves as a comprehensive benchmark for padel, fostering advancements in various applications such as cross-sport analysis, injury prevention, tactical evaluation, ball trajectory modelling, and real-time video processing.