10.5281/zenodo.6560823
https://zenodo.org/records/6560823
oai:zenodo.org:6560823
Ciampi, Luca
Luca
Ciampi
0000-0002-6985-0439
Institute of Information Science and Technologies (ISTI-CNR), Pisa, Italy
Santiago, Carlos
Carlos
Santiago
0000-0002-4737-0020
Instituto Superior Técnico (LARSyS/IST), Lisbon, Portugal
Costeira, Joao Paulo
Joao Paulo
Costeira
0000-0001-6769-2935
Instituto Superior Técnico (LARSyS/IST), Lisbon, Portugal
Gennaro, Claudio
Claudio
Gennaro
0000-0002-3715-149X
Institute of Information Science and Technologies (ISTI-CNR), Pisa, Italy
Amato, Giuseppe
Giuseppe
Amato
0000-0003-0171-4315
Institute of Information Science and Technologies (ISTI-CNR), Pisa, Italy
Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas
Zenodo
2022
object detection
vehicle detection
vehicle segmentation
vehicle counting
domain shift
Ciampi, Luca
Luca
Ciampi
0000-0002-6985-0439
Institute of Information Science and Technologies (ISTI-CNR), Pisa, Italy
Gennaro, Claudio
Claudio
Gennaro
0000-0002-3715-149X
Ciampi, Luca
Luca
Ciampi
0000-0002-6985-0439
Institute of Information Science and Technologies (ISTI-CNR), Pisa, Italy
Gennaro, Claudio
Claudio
Gennaro
0000-0002-3715-149X
2022-05-18
eng
10.5220/0010303401850195
10.5281/zenodo.6560822
https://zenodo.org/communities/ai4eu
https://zenodo.org/communities/ai4media
https://zenodo.org/communities/eu
1.0
Open Data Commons Attribution License v1.0
The Dataset
A collection of images of parking lots for vehicle detection, segmentation, and counting.
Each image is manually labeled with pixel-wise masks and bounding boxes localizing vehicle instances.
The dataset includes about 250 images depicting several parking areas describing most of the problematic situations that we can find in a real scenario: seven different cameras capture the images under various weather conditions and viewing angles. Another challenging aspect is the presence of partial occlusion patterns in many scenes such as obstacles (trees, lampposts, other cars) and shadowed cars.
The main peculiarity is that images are taken during the day and the night, showing utterly different lighting conditions.
We suggest a three-way split (train-validation-test). The train split contains images taken during the daytime while validation and test splits include images gathered at night.
In line with these splits we provide some annotation files:
train_coco_annotations.json and val_coco_annotations.json --> JSON files that follow the golden standard MS COCO data format (for more info see https://cocodataset.org/#format-data) for the training and the validation splits, respectively. All the vehicles are labeled with the COCO category 'car'. They are suitable for vehicle detection and instance segmentation.
train_dot_annotations.csv and val_dot_annotations.csv --> CSV files that contain xy coordinates of the centroids of the vehicles for the training and the validation splits, respectively. Dot annotation is commonly used for the visual counting task.
ground_truth_test_counting.csv --> CSV file that contains the number of vehicles present in each image. It is only suitable for testing vehicle counting solutions.
Citing our work
If you found this dataset useful, please cite the following paper
@inproceedings{Ciampi_visapp_2021,
doi = {10.5220/0010303401850195},
url = {https://doi.org/10.5220%2F0010303401850195},
year = 2021,
publisher = {{SCITEPRESS} - Science and Technology Publications},
author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato},
title = {Domain Adaptation for Traffic Density Estimation},
booktitle = {Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}
}
and this Zenodo Dataset
@dataset{ciampi_ndispark_6560823,
author = {Luca Ciampi and Carlos Santiago and Joao Costeira and Claudio Gennaro and Giuseppe Amato},
title = {{Night and Day Instance Segmented Park (NDISPark) Dataset: a Collection of Images taken by Day and by Night for Vehicle Detection, Segmentation and Counting in Parking Areas}},
month = may,
year = 2022,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.6560823},
url = {https://doi.org/10.5281/zenodo.6560823}
}
Contact Information
If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it
European Commission
10.13039/501100000780
951911
A European Excellence Centre for Media, Society and Democracy
European Commission
10.13039/501100000780
825619
A European AI On Demand Platform and Ecosystem