In top-level sport, the winner is determined by small details that may seem insignificant to the uneducated eye, but are actually fundamental to gaining ground on others. In
swimming in particular, many finals of important competitions end up with a difference of less than a tenth of a second between the leaders. The reasons bringing victory can be
very varied because they concern the individual physique of the swimmers, their mental and physical preparation, their understanding of the swimming style of their competitors,
and many other things. Understanding them is crucial to winning: this is the role of the sports coaches. They will study with precision what can allow their swimmer to be the most
efficient.
The first step in the analysis of training and races is information extraction. In this thesis, we are particularly interested in swimming competitions. Our goal is to generate an
automatic race report. This would free up an invaluable amount of time for coaches, and would also allow for extensive analysis of competitions. Such technology would also
improve the detection of potential talent through the systematic analysis of all amateur competitions.
Computer vision learning methods will be explored to get the best out of the videos. We will also explore image analysis with small amounts of data.