首个面向足球运动员检测的合成数据集，提升复杂场景下模型泛化能力。
SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection
- 基于合成数据生成多样光照、纹理与运动模糊，模拟真实比赛复杂场景。
- 在带运动模糊的图像上，性能超越真实数据集；预训练时显著提升检测效果。
- 适合需要增强鲁棒性或缺乏标注数据的足球视频分析研究者。
在足球视频分析中，球员检测对识别关键事件和重建战术位置至关重要。由于球员数量多、频繁遮挡及版权限制，现有数据集（如SoccerNet-Tracking和SportsMOT）种类有限，多样性不足，难以支撑算法在多样化场景下的适应。为此，我们构建了首个专用于合成足球运动员检测的SoccerSynth-Detection数据集，涵盖多种随机光照、纹理及模拟摄像机运动模糊。通过Yolov8n模型在真实数据集（SoccerNet-Tracking、SportsMoT）上的迁移测试表明，该合成数据集性能可媲美真实数据，在含运动模糊的图像中表现更优；预训练实验也证实其能显著提升算法整体性能。本工作展示了合成数据在足球视频分析领域替代真实数据进行算法训练的巨大潜力。
原文摘要 · Abstract (English)
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.
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