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
} }
Citation