GolfDB: A Video Database for Golf Swing Sequencing
This work provides a benchmark for golf swing analysis, facilitating biomechanical studies and potential mobile applications, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of analyzing golf swings by introducing a video database called GolfDB with 1400 labeled videos and a baseline deep neural network, SwingNet, which achieved 76.1% average detection accuracy for eight key events and 91.8% for six events.
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.