  Identifying players in video is a foundational step in computer vision-based
sports analytics. Obtaining player identities is essential for analyzing the
game and is used in downstream tasks such as game event recognition.
Transformers are the existing standard in Natural Language Processing (NLP) and
are swiftly gaining traction in computer vision. Motivated by the increasing
success of transformers in computer vision, in this paper, we introduce a
transformer network for recognizing players through their jersey numbers in
broadcast National Hockey League (NHL) videos. The transformer takes temporal
sequences of player frames (also called player tracklets) as input and outputs
the probabilities of jersey numbers present in the frames. The proposed network
performs better than the previous benchmark on the dataset used. We implement a
weakly-supervised training approach by generating approximate frame-level
labels for jersey number presence and use the frame-level labels for faster
training. We also utilize player shifts available in the NHL play-by-play data
by reading the game time using optical character recognition (OCR) to get the
players on the ice rink at a certain game time. Using player shifts improved
the player identification accuracy by 6%.
