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Re-identifying individuals in unconstrained environments remains an openchallenge in computer vision. We introduce the Muddy Racer re-IDentificationDataset (MUDD), the first large-scale benchmark for matching identities ofmotorcycle racers during off-road competitions. MUDD exhibits heavy mudocclusion, motion blurring, complex poses, and extreme lighting conditionspreviously unseen in existing re-id datasets. We present an annotationmethodology incorporating auxiliary information that reduced labeling time byover 65%. We establish benchmark performance using state-of-the-art re-idmodels including OSNet and ResNet-50. Without fine-tuning, the best modelsachieve only 33% Rank-1 accuracy. Fine-tuning on MUDD boosts results to 79%Rank-1, but significant room for improvement remains. We analyze the impact ofreal-world factors including mud, pose, lighting, and more. Our work exposesopen problems in re-identifying individuals under extreme conditions. We hopeMUDD serves as a diverse and challenging benchmark to spur progress in robustre-id, especially for computer vision applications in emerging sportsanalytics. All code and data can be found at https://github.com/JacobTyo/MUDD.

MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions