  The golf swing is a complex movement requiring considerable full-body
coordination to execute proficiently. As such, it is the subject of frequent
scrutiny and extensive biomechanical analyses. In this paper, we introduce the
notion of golf swing sequencing for detecting key events in the golf swing and
facilitating golf swing analysis. To enable consistent evaluation of golf swing
sequencing performance, we also introduce the benchmark database GolfDB,
consisting of 1400 high-quality golf swing videos, each labeled with event
frames, bounding box, player name and sex, club type, and view type.
Furthermore, to act as a reference baseline for evaluating golf swing
sequencing performance on GolfDB, we propose a lightweight deep neural network
called SwingNet, which possesses a hybrid deep convolutional and recurrent
neural network architecture. SwingNet correctly detects eight golf swing events
at an average rate of 76.1%, and six out of eight events at a rate of 91.8%. In
line with the proposed baseline SwingNet, we advocate the use of
computationally efficient models in future research to promote in-the-field
analysis via deployment on readily-available mobile devices.
