Video-Based Reconstruction of the Trajectories Performed by Skiers

TL;DR

This paper presents a novel video-based method using deep learning to reconstruct skier trajectories from uncalibrated videos, aiding training and broadcasting without relying on geo-localized sensors.

Contribution

The authors introduce a pipeline that reconstructs skier trajectories from uncalibrated videos using deep learning, applicable to various skiing disciplines in real-world settings.

Findings

Successful reconstruction of skier trajectories from broadcast and smartphone videos.

Effective visualization of athlete motion without camera calibration.

Potential to enhance training and broadcasting analysis.

Abstract

Trajectories are fundamental in different skiing disciplines. Tools enabling the analysis of such curves can enhance the training activity and enrich the broadcasting contents. However, the solutions currently available are based on geo-localized sensors and surface models. In this short paper, we propose a video-based approach to reconstruct the sequence of points traversed by an athlete during its performance. Our prototype is constituted by a pipeline of deep learning-based algorithms to reconstruct the athlete's motion and to visualize it according to the camera perspective. This is achieved for different skiing disciplines in the wild without any camera calibration. We tested our solution on broadcast and smartphone-captured videos of alpine skiing and ski jumping professional competitions. The qualitative results achieved show the potential of our solution.