Tracking Skiers from the Top to the Bottom
Matteo Dunnhofer Luca Sordi Niki Martinel Christian Micheloni
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.