The general idea of the project was the creation of a football analytics system, which is based on the processing of video from football training sessions and games.
The idea of combining the capabilities of an AI and the sports domain is quite new and not fully explored. Only the professional sports clubs are using an AI on the daily basis, that is why we were eager to cooperate with the founder of a football school who wanted to integrate an AI solution into the daily activities of the coaches. The analytics takes various actions of the players on the field (the number of passes, shots, distance covered, etc.) into account. Based on those actions, the analytics system should create an overall statistics list for each player.
We started the project with a PoC (proof of concept). The main task was to develop and test the possible approaches (different AI models and ways for statistics calculation). At the initial stage, we put forward several hypotheses and selected possible tools (AI models) that we wanted to test.
In order to understand how the whole system works, we should start with some basic use-case scenario on how the user (coach) interacts with the system:
During the PoC stage, we’ve inspected several ML algorithms for pose and face detection. We’ve made a documentation that included project overview, use-case scenarios, preliminary architecture, and functionality list. By the end of the PoC, we were ready for the start of the project by combining the ML algorithms together and integrating them into a web application.
One of the most crucial requirements we also should remember about was the speed of the recognition process. The speed was a strict requirement for several reasons:
As for the client, the main focus was on bringing more interactivity between the parents and the results of their children. By receiving an analytical overview of their kids performance, they become more engaged in the whole training process.