@inproceedings{Shao_WeRef_2025,
	title={{WeRef:} {An} {Open-source} {and} {Extensible} {Dataset} {for} {Referee} {Gesture} {Recognition} {in} {RoboCup}},
	author={Shao, Zisen and Hanna, Josiah P.},
	year={2025},
	month={July},
	booktitle={RoboCup-2025: Robot Soccer World Cup XXVIII},
	abstract={
		Visual recognition of referee gestures is essential for fully autonomous robot soccer, yet progress in deep learning approaches has been hindered by the absence of a public, standardized dataset and base- line. In this paper, we present WeRef, an open-source synthetic data generation pipeline for RoboCup referee gestures built on the Webots simulator, along with a large-scale synthetic dataset. Our pipeline auto- matically randomizes human models, backgrounds, lighting conditions, obstacle presence, and camera viewpoints to produce diverse data with- out manual labeling. We evaluate WeRef on real competition data us- ing a 2D CNN with GRU classifier. We show that synthetic samples generated by WeRef effectively augment limited real data, substantially reducing the need for costly data collection and improving recognition accuracy when training on a combination of synthetic and real data. By releasing both the generation software and the resulting dataset, we provide a scalable, open-source framework to facilitate the devel- opment of referee gesture recognition in the RoboCup Standard Plat- form League (SPL). The WeRef pipeline and dataset are available at https://github.com/ZisenShao/WeRef.
	},
}