  Trajectory estimation is a fundamental component of racket sport analytics,
as the trajectory contains information not only about the winning and losing of
each point, but also how it was won or lost. In sports such as badminton,
players benefit from knowing the full 3D trajectory, as the height of
shuttlecock or ball provides valuable tactical information. Unfortunately, 3D
reconstruction is a notoriously hard problem, and standard trajectory
estimators can only track 2D pixel coordinates. In this work, we present the
first complete end-to-end system for the extraction and segmentation of 3D
shuttle trajectories from monocular badminton videos. Our system integrates
badminton domain knowledge such as court dimension, shot placement, physical
laws of motion, along with vision-based features such as player poses and
shuttle tracking. We find that significant engineering efforts and model
improvements are needed to make the overall system robust, and as a by-product
of our work, improve state-of-the-art results on court recognition, 2D
trajectory estimation, and hit recognition.
