SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection

2025-01-16

AI Summary

To address poor generalization in football video analysis caused by player occlusion, scarcity of diverse real-world data, and associated copyright constraints, this paper introduces the first high-fidelity synthetic dataset specifically designed for athlete detection. The dataset uniquely models football-specific visual factors—including stochastic illumination variations, jersey texture perturbations, and camera-induced motion blur—to mitigate reliance on real data while preserving domain fidelity. Leveraging this dataset, we conduct transfer learning and pretraining experiments using YOLOv8n. Results demonstrate that the model achieves significantly higher detection accuracy on motion-blurred frames compared to SoccerNet-Tracking and SportsMOT. Moreover, pretraining yields substantial overall performance gains, empirically validating synthetic data as a viable and effective substitute for real annotations. This work establishes a robust foundation for downstream tasks such as critical event detection and tactical analysis in sports analytics.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Player Occlusion
• Dataset Scarcity
• Lack of Diversity

Innovation

• SoccerSynth-Detection
• Synthetic Data
• Enhanced Recognition