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

• URL: http://arxiv.org/abs/2311.08488v1
• Date: Tue, 14 Nov 2023 19:31:19 GMT
• Title: MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions
• Abstract summary: 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.
• Score: 38.719032057630024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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