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--- |
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license: cc0-1.0 |
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task_categories: |
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- image-segmentation |
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- object-detection |
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task_ids: |
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- semantic-segmentation |
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- instance-segmentation |
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tags: |
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- automotive |
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- autonomous driving |
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- synthetic |
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- safe ai |
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- validation |
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- pedestrian detection |
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- 2d object-detection |
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- 3d object-detection |
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- semantic-segmentation |
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- instance-segmentation |
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pretty_name: VALERIE22 |
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size_categories: |
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- 1K<n<10K |
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--- |
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# VALERIE22 - A photorealistic, richly metadata annotated dataset of urban environments |
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## Dataset Description |
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- **Paper:** tba |
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- **Point of Contact:** korbinian.hagn@intel.com |
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### Dataset Summary |
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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. |
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### Supported Tasks and Leaderboards |
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- pedestrian detection |
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- 2d object-detection |
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- 3d object-detection |
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- semantic-segmentation |
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- instance-segmentation |
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- ai-validation |
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## Dataset Structure |
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- VALERIE22 |
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- intel_results_sequence_0050 |
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- ground-truth |
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- 2d-bounding-box_json |
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- car-camera000-0000-{UUID}-0000.json |
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- 3d-bounding-box_json |
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- car-camera000-0000-{UUID}-0000.json |
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- class-id_png |
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- car-camera000-0000-{UUID}-0000.png |
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- general-globally-per-frame-analysis_json |
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- car-camera000-0000-{UUID}-0000.json |
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- car-camera000-0000-{UUID}-0000.csv |
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- semantic-group-segmentation_png |
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- car-camera000-0000-{UUID}-0000.png |
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- semantic-instance-segmentation_png |
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- car-camera000-0000-{UUID}-0000.png |
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- car-camera000-0000-{UUID}-0000 |
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- {Entity-ID} |
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- metadata |
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- car-camera000-0000-{UUID}-0000.json |
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- sensor |
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- camera |
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- left |
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- png |
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- car-camera000-0000-{UUID}-0000.png |
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- png_distorted |
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- car-camera000-0000-{UUID}-0000.png |
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- intel_results_sequence_0052 |
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- intel_results_sequence_0054 |
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- intel_results_sequence_0057 |
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- intel_results_sequence_0058 |
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- intel_results_sequence_0059 |
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- intel_results_sequence_0060 |
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- intel_results_sequence_0062 |
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### Data Splits |
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Train/Validation/Test splits are provided |
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### Licensing Information |
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Creative Commons Zero v1.0 Universal |
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### Citation Information |
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Relevant publications: |
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``` |
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@misc{grau2023valerie22, |
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title={VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments}, |
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author={Oliver Grau and Korbinian Hagn}, |
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year={2023}, |
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eprint={2308.09632}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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@inproceedings{hagn2022increasing, |
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title={Increasing pedestrian detection performance through weighting of detection impairing factors}, |
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author={Hagn, Korbinian and Grau, Oliver}, |
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booktitle={Proceedings of the 6th ACM Computer Science in Cars Symposium}, |
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pages={1--10}, |
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year={2022} |
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} |
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@inproceedings{hagn2022validation, |
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title={Validation of Pedestrian Detectors by Classification of Visual Detection Impairing Factors}, |
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author={Hagn, Korbinian and Grau, Oliver}, |
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booktitle={European Conference on Computer Vision}, |
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pages={476--491}, |
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year={2022}, |
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organization={Springer} |
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} |
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@incollection{grau2022variational, |
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title={A variational deep synthesis approach for perception validation}, |
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author={Grau, Oliver and Hagn, Korbinian and Syed Sha, Qutub}, |
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booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety}, |
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pages={359--381}, |
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year={2022}, |
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publisher={Springer International Publishing Cham} |
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} |
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@incollection{hagn2022optimized, |
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title={Optimized data synthesis for DNN training and validation by sensor artifact simulation}, |
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author={Hagn, Korbinian and Grau, Oliver}, |
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booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety}, |
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pages={127--147}, |
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year={2022}, |
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publisher={Springer International Publishing Cham} |
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} |
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@inproceedings{syed2020dnn, |
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title={DNN analysis through synthetic data variation}, |
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author={Syed Sha, Qutub and Grau, Oliver and Hagn, Korbinian}, |
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booktitle={Proceedings of the 4th ACM Computer Science in Cars Symposium}, |
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pages={1--10}, |
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year={2020} |
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} |
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``` |
