MonoTrack: Shuttle trajectory reconstruction from monocular badminton video

TL;DR

This paper introduces MonoTrack, an end-to-end system that reconstructs 3D shuttle trajectories from monocular badminton videos, integrating domain knowledge and vision features to enhance sports analytics.

Contribution

MonoTrack is the first system to extract and segment 3D shuttle trajectories from monocular badminton videos, combining physical laws, court knowledge, and vision-based cues.

Findings

Improved 2D trajectory estimation accuracy

Enhanced court recognition performance

Better hit recognition results

Abstract

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.