超越泥泞：越野赛车中的计算机视觉数据集与基准
Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing
AI总结：
本文针对越野赛车场景中泥泞等极端条件下的文本识别和行人重识别难题，构建了RND和MUDD两个真实世界数据集，并基于最先进模型建立基准，揭示现有技术在此类复杂环境下的性能局限与未来改进方向。
摘要：
尽管光学字符识别（OCR）和计算机视觉系统取得了重大进展，但在不受约束的真实世界环境中拍摄的图像中，稳健地识别文本和识别人物仍然是一个持续的挑战。然而，在视觉系统的实际应用中必须克服这些障碍，例如在越野赛车赛事中拍摄的照片里识别赛车手。为此，我们引入了两个具有挑战性的新真实世界数据集——越野摩托车赛车手号码数据集（RND）和泥泞赛车手重新识别数据集（MUDD）——以突显当前方法的不足，并推动极端条件下的OCR和行人重识别（ReID）的发展。这两个数据集包含在越野比赛中拍摄的6,300多张图像，这些图像展现了多种破坏现代视觉系统效果的因素，即泥泞、复杂姿态和运动模糊。我们使用最先进的模型在这两个数据集上建立了基准性能。现成模型迁移效果很差，在文本检测上仅达到15%的端到端（E2E）F1分数，在ReID上仅达到33%的rank-1准确率。微调带来了显著改进，将模型性能提升至E2E文本检测53%的F1分数和ReID 79%的rank-1准确率，但仍未达到良好的性能水平。我们的分析揭示了真实世界OCR和ReID中存在的开放性问题，需要领域针对性的技术。通过这些数据集和对模型局限性的分析，我们旨在促进处理泥泞和复杂姿态等真实世界条件的创新，以推动稳健计算机视觉的发展。所有数据均来自PerformancePhoto.co，这是一个供专业赛车摄影师、赛车手和粉丝使用的网站。表现最佳的文本检测和ReID模型已部署在该平台上，以支持实时比赛照片搜索。
英文摘要：
Despite significant progress in optical character recognition (OCR) and computer vision systems, robustly recognizing text and identifying people in images taken in unconstrained \emph{in-the-wild} environments remain an ongoing challenge. However, such obstacles must be overcome in practical applications of vision systems, such as identifying racers in photos taken during off-road racing events. To this end, we introduce two new challenging real-world datasets - the off-road motorcycle Racer Number Dataset (RND) and the Muddy Racer re-iDentification Dataset (MUDD) - to highlight the shortcomings of current methods and drive advances in OCR and person re-identification (ReID) under extreme conditions. These two datasets feature over 6,300 images taken during off-road competitions which exhibit a variety of factors that undermine even modern vision systems, namely mud, complex poses, and motion blur. We establish benchmark performance on both datasets using state-of-the-art models. Off-the-shelf models transfer poorly, reaching only 15% end-to-end (E2E) F1 score on text spotting, and 33% rank-1 accuracy on ReID. Fine-tuning yields major improvements, bringing model performance to 53% F1 score for E2E text spotting and 79% rank-1 accuracy on ReID, but still falls short of good performance. Our analysis exposes open problems in real-world OCR and ReID that necessitate domain-targeted techniques. With these datasets and analysis of model limitations, we aim to foster innovations in handling real-world conditions like mud and complex poses to drive progress in robust computer vision. All data was sourced from PerformancePhoto.co, a website used by professional motorsports photographers, racers, and fans. The top-performing text spotting and ReID models are deployed on this platform to power real-time race photo search.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
