SwimTrack: Swimmers and Stroke Rate Detection in Elite Race Videos

See the MediaEval 2022 webpage for information on how to register and participate.

Task Description

The SwimTrack represents a series of 5 multimedia tasks related to swimming video analysis from elite competition recordings. These tasks are related to video, image, and audio analysis which may be achieved independently. But when solved altogether, they form a grand challenge to provide sport federations and coaches with novel methods to assesand enhance swimmers’ performance, in particular related tTask Descriptiono stroke rate and length analysis. The five proposed tasks are as follows:

Motivation and background

Swimming has a long tradition of being analyzed (e.g., race time, lap time, rankings) due to official time recording devices. There is however little information at a more detailed level, i.e., within laps or on the swimmers’ speed and real-time motion, except for manually annotated datasets. The goal of the SwimTrack-v1 challenge is to push the envelope of systems that accurately track swimmers’ motion in a reliable way during elite competitions. Current state of the art in multi-object tracking is limited by the unusual nature of a swimmer’s motion and large noise generated by the water. This first version of the challenge is divided into 5 independent tasks. Each of them contains its own set of input data, output format, and an evaluation metric.

Target group

This task is targeting computer vision and machine learning scientists, researchers and students with a particular interest in processing sports-related multimodal content.


The data set consists of swimming videos recorded during national and international competitions. Videos cover all the 4 swimming styles (Freestyle, Backstroke, Breaststroke, Butterfly, Medley), both genders (female, male) and principal race lengths (50m, 100m, 200m, 400m) for 50m-long swimming pools. They cover all the swimming phases (e. g.,standing, diving, underwater, return and finish). The camera view parameters vary from static wide angle to zoomed + moving using various camera types (GoPro 8, Blackmagic Pocket 6K and Panasonic HC-V750). Resolutions range from HD to 4K with variable frame rates across the recordings (between 25fps and up to 50fps). Despite those differences, the provided videos share the same MP4 format resulting from the same compression algorithm. We will make available multimedia data from 10 competitions recorded continuously, from a fixed spot on the stands, with or without any pan and zoom.

Evaluation methodology

Participants’ proposal will be evaluated when submissions of solutions to our website will be permitted. This website will dynamically calculate the score for the TEST dataset of each task. If all tasks have been addressed, a general “grand challenge” score will be calculated. As stated in the introduction, we will however limit the number of times participant can submit solutions. The metrics for each task will be as follows:

Quest for insight

Here are several research questions related to this challenge that participants can strive to answer in order to go beyond just looking at the evaluation metrics:

Participant information

Please contact your task organizers with any questions on these points.

[1] Nicolas Jacquelin, Romain Vuillemot, and Stefan Duffner. 2021. Detecting Swimmers in Unconstrained Videos with Few Training Data. 8th Workshop on Machine Learning and Data Mining for Sports Analytics (Sept. 2021).

[2] T. F. H. Runia, C. G. M. Snoek, and A. W. M. Smeulders. 2018. Real-World Repetition Estimation by Div, Grad and Curl. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9009–9017.

[3] Timothy Woinoski, Alon Harell, and I. Bajić. 2020. Towards Automated Swimming Analytics Using Deep Neural Networks. ArXiv (2020).

Task organizers

Contact: romain.vuillemot (at) ec-lyon.fr

Task Schedule