Transductive Weakly-Supervised Player Detection using Soccer Broadcast Videos

Player detection lays the foundation for many applications in the field of sports analytics including player recognition, player tracking, and activity detection. In this work, we study player detection in continuous long shot broadcast videos. Broadcast match videos are easy to obtain, and detection on these videos is much more challenging. We propose a transductive approach for player detection that treats it as a domain adaptation problem. We show that instance-level domain labels are significant for sufficient adaptation in the case of soccer broadcast videos. An efficient multi-model greedy labelling scheme based on visual features is proposed to annotate domain labels on bounding box predictions made by our inductive model. We use reliable instances from the inductive model inferences to train a transductive copy of the model. We create and release a fully annotated player detection dataset comprising soccer broadcast videos from the FIFA 2018 World Cup matches to evaluate our method. Our method shows significant improvements in player detection to the baseline and existing state-of-the-art methods on our dataset. We show, on average, a 16 point improvement in mAP for soccer broadcast videos by annotating domain labels for around a 100 samples per video.