Winter sports such as skiing, bobsleigh, ice-skating and ice-hockey are popular winter activities. To improvehuman well-being and sociality, different winter sport federations want to promote such sports by increasing the people engagement in them. This is mostly obtained by the broadcasting of professional competitions. Indeed, the TV transmission of only alpine and nordic skiing competitions attracts around 6 billions of viewers (source: International Ski Federation – FIS - 2019/2020 season reports) including many new potential practitioners.

Increased interest towards the winter disciplines can be achieved by enhancing the viewing experience of the spectators through richer broadcasting contents and higher athlete performance, and these can be driven by yet-to-come video technologies. The most common solutions available today to analyze the athlete performance are based on sensors like IMUs or GNSS. Image and video analytics is not a predominant technology in the winter sports domain yet, even though a large amount of visual data is available through broadcast videos or can be generated at a cheap cost with standard cameras. We believe this gap is in part due to the challenging settings that the snowy and icy environments set when they are imaged with continuously moving cameras. These issues must be then considered in relation to the real-time processing requirements of broadcasting applications or the decision-making processes performed during the trainings. We believe that the problems arising in these domains offer particular and stimulating challenges that would lead to relevant contributions to the computer vision field.

For these motivations, the goal of our workshop is twofold: on one hand to promote the employment of computer vision and AI solutions in the winter sports industry, by presenting the latest research solutions for winter sports-related problems; and at the same time, we would like to stimulate the interest of the computer vision and AI audience with new and interesting problems that could lead to the engagement of researchers and the development of new solutions. Hence, we invite researchers and engineers interested in these topics to join our workshop, submitting ongoing and recently published ideas, demos, and applications in support of increasing the effectiveness and efficiency of computer vision technologies in the winter sports domain.

Call for Papers

Research papers are solicited in, but not limited to, the following topic areas:

  • Machine learning solutions for video understanding or activity recognition regarding winter sports

  • Pose estimation of athletes

  • Evaluation and measurement of athlete performance

  • Performance forecasting

  • Detection/evaluation/prevention of injuries in winter sports with computer vision

  • Crowd and spectators monitoring

  • Augmented/virtual reality for winter sports and fan engagement

  • Applications of computer vision/AI to winter sports (skiing, ice-hockey, ice-skating, biathlon, bobsleigh, luge, curling, etc.)

  • Image/video understanding in winter/harsh weather conditions

  • Camera pose estimation in broadcast videos

  • Video-based trajectory reconstruction and analysis

  • Winter scene reconstruction from images/videos

  • Snow/ice measurements and analysis with computer vision

  • Real-time processing algorithms

  • Fusion of image/video data and other sensor data

  • Datasets, benchmarks and annotations of winter sport data


Please see the Submissions and Dates page for details about the submissions.

Invited Speakers

TBA

Supporting Organizations