# Video-Based Reconstruction of the Trajectories Performed by Skiers

arXiv: https://arxiv.org/abs/2112.09647 · HTML: https://mlsanity.com/paper/2112.09647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Intra-frame Object Tracking by Deblatting](https://mlsanity.com/paper/1905.03633/) (2019)
- [Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator](https://mlsanity.com/paper/1909.08487/) (2019)
- [Motion Capture from Pan-Tilt Cameras with Unknown Orientation](https://mlsanity.com/paper/1908.11676/) (2019)

## Abstract (arXiv)

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.