SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection
Journal:
arXiv
Published Date:
Jan 16, 2025
Abstract
In soccer video analysis, player detection is essential for identifying key
events and reconstructing tactical positions. The presence of numerous players
and frequent occlusions, combined with copyright restrictions, severely
restricts the availability of datasets, leaving limited options such as
SoccerNet-Tracking and SportsMOT. These datasets suffer from a lack of
diversity, which hinders algorithms from adapting effectively to varied soccer
video contexts. To address these challenges, we developed
SoccerSynth-Detection, the first synthetic dataset designed for the detection
of synthetic soccer players. It includes a broad range of random lighting and
textures, as well as simulated camera motion blur. We validated its efficacy
using the object detection model (Yolov8n) against real-world datasets
(SoccerNet-Tracking and SportsMoT). In transfer tests, it matched the
performance of real datasets and significantly outperformed them in images with
motion blur; in pre-training tests, it demonstrated its efficacy as a
pre-training dataset, significantly enhancing the algorithm's overall
performance. Our work demonstrates the potential of synthetic datasets to
replace real datasets for algorithm training in the field of soccer video
analysis.
