  Puck localization is an important problem in ice hockey video analytics
useful for analyzing the game, determining play location, and assessing puck
possession. The problem is challenging due to the small size of the puck,
excessive motion blur due to high puck velocity and occlusions due to players
and boards. In this paper, we introduce and implement a network for puck
localization in broadcast hockey video. The network leverages expert NHL
play-by-play annotations and uses temporal context to locate the puck. Player
locations are incorporated into the network through an attention mechanism by
encoding player positions with a Gaussian-based spatial heatmap drawn at player
positions. Since event occurrence on the rink and puck location are related, we
also perform event recognition by augmenting the puck localization network with
an event recognition head and training the network through multi-task learning.
Experimental results demonstrate that the network is able to localize the puck
with an AUC of $73.1 \%$ on the test set. The puck location can be inferred in
720p broadcast videos at $5$ frames per second. It is also demonstrated that
multi-task learning with puck location improves event recognition accuracy.
