SVIRO Dataset and Benchmark

SVIRO is a Synthetic dataset for Vehicle Interior Rear seat Occupancy detection and classification. The dataset consists of 25.000 sceneries across ten different vehicles and we provide several simulated sensor inputs and ground truth data. SVIRO can be used to evaluate machine learning models for their generalization capacities and reliability when trained on a limited number of variations. Our goal was to provide a common benchmark to test approaches when resources are limited, as is common for engineering applications.

We offer an automatic evaluation server and public leaderboard to compare different methods and models.

RGB image

Infrared immitation

Depth image

Mask

Keypoints

Bounding boxes

We made sure to limit the amount of variations: We used identical backgrounds and textures for training, but used different ones for testing. Additionally, we used different human models, child and infant seats for each data split. Using this approach, one can test the generalization and robustness of machine learning models trained in one vehicle to a new one, for solving the same task. Consequently, the machine learning models need to generalize to previously unknown car interiors and unseen intra-class variations.

We provide RGB images, simulated infrared images and depth images. Our dataset contains bounding boxes for object detection, masks for semantic and instance segmentation and keypoints for pose estimation for each synthetic scenery, as well as images for each individual seat for classification.

10 Cars
25,000 Sceneries
5 Benchmarks

SVIRO-Illumination

Based on SVIRO, we created images for three vehicle interiors. For each vehicle, we randomly generated 250 training and 250 test scenes where each scenery was rendered under 10 different illumination and environmental conditions. This dataset can be used to investigate the effect of changing illumination and environment on the classification accuracy and robustness of machine learning models.

Citation

When using the SVIRO dataset in your research, please cite us using the following:

@INPROCEEDINGS{DiasDaCruz2020SVIRO,
  author = {Steve {Dias Da Cruz} and Oliver Wasenm\"uller and Hans-Peter Beise and Thomas Stifter and Didier Stricker},
  title = {SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark},
  booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year = {2020}
} 

When using the SVIRO-Illumination dataset in your research, please cite us using the following:

@INPROCEEDINGS{DiasDaCruz2021Illumination,
  author = {Steve {Dias Da Cruz} and Bertram Taetz and Thomas Stifter and Didier Stricker},
  title = {Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function},
  booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year = {2021}
} 

License

This work, all the datasets and benchmarks are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This means that:

  • You must give appropriate credit,
  • You may not use the material for commercial purposes,
  • If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

Acknowledgement

Steve Dias Da Cruz is supported by the Luxembourg National Research Fund (FNR) under the grant number 13043281. This work was partially funded by the MECO project ”Artificial Intelligence for SafetyCritical Complex Systems” and the European Union’s Horizon 2020 Program in the project VIZTA (826600). We want to thank Michelle Gosha for the help during the data generation process.

News

IEEE WACV 2021 – Paper accepted

Our paper entitled “Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function” has been accepted for publication at IEEE Winter Conference on Applications of Computer Vision (WACV 2021). We present an image normalization method based on a new strategy for the

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IEEE WACV 2020 – Paper accepted

Our paper entitled “SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark” has been accepted for publication at IEEE Winter Conference on Applications of Computer Vision (WACV 2020). This paper gives a detailed overview about SVIRO, some baseline investigations

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ACM CSCS 2019 – Extended Abstract

We presented an extended abstract entitled “An Overview of the SVIRO Dataset and Benchmark” at the ACM Computer Science in Cars Symposium (CSCS 2019) held at the DFKI in Kaiserslautern in October. This work gives a short introduction about our

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