sgphd/occluded-e-scooter-rider-dataset

E-Scooter Rider Detection and Classification in Dense Urban Environments

Partially Occluded E-Scooter Rider Detection Dataset

E-Scooter Rider Detection and Classification in Dense Urban Environments

Partially Occluded E-Scooter Rider Detection Dataset used in "E-Scooter Rider Detection and Classification in Dense Urban Environments" Gilroy et al 2022.

This dataset contains 1,130 images including 543 e-scooter rider instances and 587 other vulnerable road user instances, for the characterization of detection and classification model performance for partially occluded e-scooter riders. Vulnerable road user instances are occluded by a diverse mix of objects across a range of occlusion levels from 0 to 99% occluded.

Images are annotated using the objective occlusion level annotation method described in “Pedestrian Occlusion Level Classification using Keypoint Detection and 2D Body Surface Area Estimation” Gilroy et al 2021.

Download Dataset Here

• E-Scooter Rider Dataset Download Link

Please cite the following work

Results in Engineering 2022

@article{gilroy2022scooter,
  title={E-Scooter Rider detection and classification in dense urban environments},
  author={Gilroy, Shane and Mullins, Darragh and Jones, Edward and Parsi, Ashkan and Glavin, Martin},
  journal={Results in Engineering},
  volume={16},
  pages={100677},
  year={2022},
  publisher={Elsevier}
}

Pattern Recognition Letters 2022

@article{gilroy2022objective,
  title={An objective method for pedestrian occlusion level classification},
  author={Gilroy, Shane and Glavin, Martin and Jones, Edward and Mullins, Darragh},
  journal={Pattern Recognition Letters},
  volume={164},
  pages={96--103},
  year={2022},
  publisher={Elsevier}
}

ICCV2021

@inproceedings{gilroy2021pedestrian,
  title={Pedestrian Occlusion Level Classification using Keypoint Detection and 2D Body Surface Area Estimation},
  author={Gilroy, Shane and Glavin, Martin and Jones, Edward and Mullins, Darragh},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={3833--3839},
  year={2021}
}