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---
license: cc-by-4.0
task_categories:
- image-segmentation
- object-detection
task_ids:
- semantic-segmentation
- instance-segmentation
tags:
- automotive
- autonomous driving
- synthetic
- safe ai
- validation
- pedestrian detection
- 2d object-detection
- 3d object-detection
- semantic-segmentation
- instance-segmentation
pretty_name: VALERIE22
size_categories:
- 1K<n<10K
---
# VALERIE22 - A photorealistic, richly metadata annotated dataset of urban environments
## Dataset Description
- **Paper:** https://arxiv.org/abs/2308.09632
- **Point of Contact:** korbinian.hagn@intel.com
### Dataset Summary
The VALERIE22 dataset was generated with the VALERIE procedural tools pipeline providing a photorealistic sensor simulation rendered from automatically synthesized scenes. The dataset provides a uniquely rich set of metadata, allowing extraction of specific scene and semantic features (like pixel-accurate occlusion rates, positions in the scene and distance + angle to the camera). This enables a multitude of possible tests on the data and we hope to stimulate research on understanding performance of DNNs.
### Supported Tasks and Leaderboards
- pedestrian detection
- 2d object-detection
- 3d object-detection
- semantic-segmentation
- instance-segmentation
- ai-validation
## Dataset Structure
```
VALERIE22
└───intel_results_sequence_0050
│ └───ground-truth
│ │ └───2d-bounding-box_json
│ │ │ └───car-camera000-0000-{UUID}-0000.json
│ │ └───3d-bounding-box_json
│ │ │ └───car-camera000-0000-{UUID}-0000.json
│ │ └───class-id_png
│ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ └───general-globally-per-frame-analysis_json
│ │ │ └───car-camera000-0000-{UUID}-0000.json
│ │ │ └───car-camera000-0000-{UUID}-0000.csv
│ │ └───semantic-group-segmentation_png
│ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ └───semantic-instance-segmentation_png
│ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ │ └───car-camera000-0000-{UUID}-0000
│ │ │ │ └───{Entity-ID}
│ └───sensor
│ │ └───camera
│ │ │ └───left
│ │ │ │ └───png
│ │ │ │ │ └───car-camera000-0000-{UUID}-0000.png
│ │ │ │ └───png_distorted
│ │ │ │ │ └───car-camera000-0000-{UUID}-0000.png
└───intel_results_sequence_0052
└───intel_results_sequence_0054
└───intel_results_sequence_0057
└───intel_results_sequence_0058
└───intel_results_sequence_0059
└───intel_results_sequence_0060
└───intel_results_sequence_0062
```
### Data Splits
Train/Validation/Test splits are provided
### Licensing Information
Creative Commons Zero v1.0 Universal
### Citation Information
Relevant publications:
```
@misc{grau2023valerie22,
title={VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments},
author={Oliver Grau and Korbinian Hagn},
year={2023},
eprint={2308.09632},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{hagn2022increasing,
title={Increasing pedestrian detection performance through weighting of detection impairing factors},
author={Hagn, Korbinian and Grau, Oliver},
booktitle={Proceedings of the 6th ACM Computer Science in Cars Symposium},
pages={1--10},
year={2022}
}
@inproceedings{hagn2022validation,
title={Validation of Pedestrian Detectors by Classification of Visual Detection Impairing Factors},
author={Hagn, Korbinian and Grau, Oliver},
booktitle={European Conference on Computer Vision},
pages={476--491},
year={2022},
organization={Springer}
}
@incollection{grau2022variational,
title={A variational deep synthesis approach for perception validation},
author={Grau, Oliver and Hagn, Korbinian and Syed Sha, Qutub},
booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},
pages={359--381},
year={2022},
publisher={Springer International Publishing Cham}
}
@incollection{hagn2022optimized,
title={Optimized data synthesis for DNN training and validation by sensor artifact simulation},
author={Hagn, Korbinian and Grau, Oliver},
booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},
pages={127--147},
year={2022},
publisher={Springer International Publishing Cham}
}
@inproceedings{syed2020dnn,
title={DNN analysis through synthetic data variation},
author={Syed Sha, Qutub and Grau, Oliver and Hagn, Korbinian},
booktitle={Proceedings of the 4th ACM Computer Science in Cars Symposium},
pages={1--10},
year={2020}
}
``` |