DOI: 10.5281/zenodo.6560822
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

description: <strong>The Dataset</strong> A collection of images of parking lots for <em>vehicle detection, segmentation, and counting</em>.<br> Each image is <em>manually</em> labeled with pixel-wise masks and bounding boxes localizing vehicle instances.<br> 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.<br> The main peculiarity is that <em>images are taken during the day and the night</em>, 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.<br> In line with these splits we provide some annotation files: <em>train_coco_annotations.json</em> and <em>val_coco_annotations.json</em> --&gt; 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<em> 'car'</em>. They are suitable for vehicle detection and instance segmentation. <em>train_dot_annotations.csv</em> and <em>val_dot_annotations.csv</em> --&gt; 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. <em>ground_truth_test_counting.csv</em> --&gt; CSV file that contains the number of vehicles present in each image. It is only suitable for testing vehicle counting solutions. <strong>Citing our work</strong> If you found this dataset useful, please cite the following paper <pre>@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} } </pre> and this Zenodo Dataset <pre>@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} } </pre> <strong>Contact Information</strong> 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