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

arXiv: https://arxiv.org/abs/2311.08488 · HTML: https://mlsanity.com/paper/2311.08488/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes](https://mlsanity.com/paper/1908.08207/) (2019)
- [Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification](https://mlsanity.com/paper/1711.07027/) (2017)
- [Unsupervised Person Re-identification: Clustering and Fine-tuning](https://mlsanity.com/paper/1705.10444/) (2017)
- [DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations](https://mlsanity.com/paper/2208.08190/) (2022)
- [Person Re-Identification by Discriminative Selection in Video Ranking](https://mlsanity.com/paper/1601.06260/) (2016)
- [Transferring a Semantic Representation for Person Re-Identification and Search](https://mlsanity.com/paper/1706.03725/) (2017)

## Built on by (most cited first, 1 of 1)

- [Contrastive Multiple Instance Learning for Weakly Supervised Person ReID](https://mlsanity.com/paper/2402.07685/) (2024)

## Abstract (arXiv)

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.