SoccerSynth-Detection：一个用于足球运动员检测的合成数据集
SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection
AI总结：
针对足球运动员检测领域真实数据集稀缺、多样性不足的问题，研究构建了含多样光照、纹理及运动模糊的合成数据集SoccerSynth-Detection，实验验证其可匹配甚至超越真实数据集的训练效果，具备替代真实数据集的潜力。
摘要：
在足球视频分析中，运动员检测是识别关键事件与重建战术位置的核心环节。球员数量众多、遮挡频繁，再加上版权限制，严重制约了可用数据集的规模，目前仅有SoccerNet-Tracking和SportsMOT等少数可选数据集。这些数据集存在多样性不足的问题，导致算法难以有效适配不同的足球视频场景。\n为应对上述挑战，我们构建了SoccerSynth-Detection——首个面向合成足球运动员检测任务的合成数据集。该数据集涵盖丰富的随机光照与纹理条件，还模拟了相机运动模糊效果。我们采用目标检测模型Yolov8n，在真实数据集SoccerNet-Tracking和SportsMOT上验证了该数据集的有效性。在迁移测试中，其表现与真实数据集相当，且在含运动模糊的图像上性能显著优于真实数据集；在预训练测试中，它展现出作为预训练数据集的效用，可显著提升算法的整体性能。本研究证明了在足球视频分析领域，合成数据集具备替代真实数据集用于算法训练的潜力。
英文摘要：
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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