Artificial intelligence and computer vision are very useful and high-quality tools in sport, as they reduce the time needed to search, collect and analyse data. However, as artificial intelligence is not yet advanced enough for slightly advanced programs and ideas, human knowledge is currently still at a great advantage. If we imagine a program that only needed to take a snapshot of a handball match and instantly return segmented data of all the individuals and their shots, perhaps the design for such a program does not seem so complex.
When we look into the design in more detail, we realise that many different approaches and work are needed to create such an application. One of the biggest basics for such an application would be the localization of players with deeply learned algorithms. Such localization relies heavily on human assistance, as the algorithms need to be physically launched. But we have to start somewhere, because that is the only way to get to the desired goals. With the help of various pre-prepared algorithms and our own knowledge, we can extract the location of players on the layout of a handball court from amateur footage of a handball match, or from three-dimensional to two-dimensional space.
The results returned by the localisation are acceptable for the conditions in which the data were captured. Due to amateur shots, the use of a single camera and poor placement, there are deviations in the mapping of the players. In perfect working conditions, where there would be several cameras placed in better positions, the results would be slightly more accurate, but never precise enough, due to the incomplete transformation from three-dimensional to two-dimensional space.
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