  Re-identifying individuals in unconstrained environments remains an open
challenge in computer vision. We introduce the Muddy Racer re-IDentification
Dataset (MUDD), the first large-scale benchmark for matching identities of
motorcycle racers during off-road competitions. MUDD exhibits heavy mud
occlusion, motion blurring, complex poses, and extreme lighting conditions
previously unseen in existing re-id datasets. We present an annotation
methodology incorporating auxiliary information that reduced labeling time by
over 65%. We establish benchmark performance using state-of-the-art re-id
models including OSNet and ResNet-50. Without fine-tuning, the best models
achieve 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 of
real-world factors including mud, pose, lighting, and more. Our work exposes
open problems in re-identifying individuals under extreme conditions. We hope
MUDD serves as a diverse and challenging benchmark to spur progress in robust
re-id, especially for computer vision applications in emerging sports
analytics. All code and data can be found at https://github.com/JacobTyo/MUDD.
