Markerless Multi-view Multi-person Tracking for Combat Sports
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We introduce a novel framework for 3D pose estimation in combat sports. Utilizing a sparse multi-camera setup, our approach employs a computer vision-based tracker to extract 2D pose predictions from each camera view, enforcing consistent tracking targets across views with epipolar constraints and long-term video object segmentation. Through a top-down transformerbased approach, we ensure high-quality 2D pose extraction. We estimate the 3D position via weighted triangulation, spline fitting and extended Kalman filtering. By employing kinematic optimization and physics-based trajectory refinement, we achieve state-of-the-art accuracy and robustness under challenging conditions such as occlusion and rapid movements. Experimental validation on diverse datasets, including a custom dataset featuring elite boxers, underscores the effectiveness of our approach. Additionally, we contribute a valuable sparring video dataset to advance research in multi-person tracking for sports.
Description
CCS Concepts: Computing methodologies → Pose Estimation; Optimization
@inproceedings{10.2312:sca.20241162,
booktitle = {Eurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters},
editor = {Zordan, Victor},
title = {{Markerless Multi-view Multi-person Tracking for Combat Sports}},
author = {Feiz, Hossein and Labbé, David and Andrews, Sheldon},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-263-9},
DOI = {10.2312/sca.20241162}
}