Intel/VALERIE22

VALERIE22数据集是通过VALERIE程序工具管道生成的，提供了丰富的元数据，允许提取特定的场景和语义特征（如像素级遮挡率、场景中的位置以及与摄像头的距离和角度）。该数据集支持多种任务，包括行人检测、2D/3D物体检测、语义分割和实例分割等。数据集包含训练、验证和测试集，分别包含13476和8406张图像。

The VALERIE22 dataset is generated via the VALERIE programmatic toolchain, and is equipped with rich metadata that enables extraction of specific scene and semantic features, such as pixel-level occlusion rate, in-scene position, distance from the camera, and angle relative to the camera. This dataset supports a variety of tasks including pedestrian detection, 2D/3D object detection, semantic segmentation, and instance segmentation, among others. The dataset comprises training, validation, and test sets, which contain 13,476 and 8,406 images respectively.

原始信息汇总

VALERIE22 数据集概述

数据集描述

数据集摘要

VALERIE22 数据集是通过 VALERIE 程序工具管道生成的，提供了从自动合成场景渲染出的逼真传感器模拟图像。该数据集提供了丰富的元数据，允许提取特定的场景和语义特征（如像素级遮挡率、场景中的位置以及与相机的距离和角度）。这使得可以对数据进行多种可能的测试，并希望促进对 DNN 性能理解的研究。

支持的任务

• 行人检测
• 2D 物体检测
• 3D 物体检测
• 语义分割
• 实例分割
• AI 验证

数据集结构

数据集结构如下：

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

数据分割

• 训练集：13476 张图像
• 验证集和测试集：8406 张图像

许可信息

CC BY 4.0

引用信息

相关出版物：

@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} }