Tracking Skiers from the Top to the Bottom

TL;DR

This paper introduces SkiTB, the largest annotated dataset for skier tracking, evaluates various tracking algorithms including a new skier-optimized baseline, and provides insights into computer vision applications in skiing performance analysis.

Contribution

The paper presents SkiTB, the first comprehensive annotated dataset for skier tracking, and evaluates multiple tracking algorithms to advance computer vision in skiing analysis.

Findings

The new skier-optimized baseline algorithm outperforms existing methods.

Evaluation results highlight the strengths and limitations of current tracking algorithms for skiing.

SkiTB dataset enables future research in skiing performance analysis.

Abstract

Skiing is a popular winter sport discipline with a long history of competitive events. In this domain, computer vision has the potential to enhance the understanding of athletes' performance, but its application lags behind other sports due to limited studies and datasets. This paper makes a step forward in filling such gaps. A thorough investigation is performed on the task of skier tracking in a video capturing his/her complete performance. Obtaining continuous and accurate skier localization is preemptive for further higher-level performance analyses. To enable the study, the largest and most annotated dataset for computer vision in skiing, SkiTB, is introduced. Several visual object tracking algorithms, including both established methodologies and a newly introduced skier-optimized baseline algorithm, are tested using the dataset. The results provide valuable insights into the applicability of different tracking methods for vision-based skiing analysis. SkiTB, code, and results are available at https://machinelearning.uniud.it/datasets/skitb.