Effect of Annotation on Multiple-Player-Tracking Algorithms

Abstract

For most people from all ages and genders, participation in sports becomes part of their life, especially participation in soccer matches, which are considered a symbol of healthy living and active attitudes of families. Analyzing soccer matches, similar to analyzing other sports, is a challenging job for the coaches and trainers, as well as for the audience, due to the fast motion of players in some situations during the match and occlusions. That is why computer vision techniques are used to tackle these problems. In this paper, we test an efficient, simple and available annotating tool to label and generate the ground truth data of the interested targets (players or the ball) on a soccer field, which helps to assess the performance of the tracking techniques and achieve their goals. This method is tested on two different sequences of soccer datasets. The annotation results are tested by using four tracking algorithms based on context-aware correlation filters. The tracking results on both sequences that were annotated by the experts and annotated by this method were very similar, which shows that this robust method outperforms the state-of-the-art annotating techniques.