Associative Embedding for Game-Agnostic Team Discrimination

Assigning team labels to players in a sport game is not a trivial task when no prior is known about the visual appearance of each team. Our work builds on a Convolutional Neural Network (CNN) to learn a descriptor, namely a pixel-wise embedding vector, that is similar for pixels depicting players from the same team, and dissimilar when pixels correspond to distinct teams. The advantage of this idea is that no per-game learning is needed, allowing efficient team discrimination as soon as the game starts. In principle, the approach follows the associative embedding framework introduced in arXiv:1611.05424 to differentiate instances of objects. Our work is however different in that it derives the embeddings from a lightweight segmentation network and, more fundamentally, because it considers the assignment of the same embedding to unconnected pixels, as required by pixels of distinct players from the same team. Excellent results, both in terms of team labelling accuracy and generalization to new games/arenas, have been achieved on panoramic views of a large variety of basketball games involving players interactions and occlusions. This makes our method a good candidate to integrate team separation in many CNN-based sport analytics pipelines.Comment: Published in CVPR 2019 workshop Computer Vision in Sports, under the name "Associative Embedding for Team Discrimination" (http://openaccess.thecvf.com/content_CVPRW_2019/html/CVSports/Istasse_Associative_Embedding_for_Team_Discrimination_CVPRW_2019_paper.html

Scene-specific classifier for effective and efficient team sport players detection from a single calibrated camera

This paper considers the detection of players in team sport scenes observed with a still or motion-compensated camera. Background-subtracted foreground masks provide easy-to-compute primary cues to identify the vertical silhouettes of moving players in the scene. However, they are shown to be too noisy to achieve reliable detections when only a single viewpoint is available, as often desired for reduced deployment cost. To circumvent this problem, our paper investigates visual classification to identify the true positives among the candidates detected by the foreground mask. It proposes an original approach to automatically adapt the classifier to the game at hand, making the classifier scene-specific for improved accuracy. Since this adaptation implies the use of potentially corrupted labels to train the classifier, a semi-naive Bayesian classifier that combines random sets of binary tests is considered as a robust alternative to boosted classification solutions. In final, our validations on two publicly released datasets prove that our proposed combination of visual and temporal cues supports accurate and reliable players’ detection in team sport scenes observed from a single viewpoint