PKLot - A robust dataset for parking lot classification

By Paulo Ricardo Lisboa de Almeida, Luiz Oliveira, Alceu de Souza Britto Jr., Eunelson Jose da Silva Junior, Alessandro Lameiras Koerich. Expert systems with applications, 2015

313 citations recorded by the source corpus.

Subfields: Building and Construction, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/753436bd4d85981a88d12af3a1321b2577dfe45a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Luca Ciampi
2. Giuseppe Amato
3. Claudio Gennaro

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Xiaogang Wang
3. Luca Ciampi
4. Giuseppe Amato
5. Claudio Gennaro
6. Ming-Hsuan Yang
7. Dacheng Tao
8. Longyin Wen

Citations in the corpus

• Where are the Blobs: Counting by Localization with Point Supervision (2018, methodology): “Our benchmark uses datasets representing different settings for object counting: Mall [15], UCSD [16], and ShanghaiTech B [17] as crowd datasets; MIT Traffic [18], and Park lot [5] as surveillance datasets; Trancos [6] as a traffic monitoring dataset; and Penguins [1] as a population monitoring data”
• Counting Vehicles with Deep Learning in Onboard UAV Imagery (2019, methodology): “It is a subset of PUCPR dataset [15], adapted by the authors for the counting task.”
• Guided Attention Network for Object Detection and Counting on Drones (2019, methodology): “The PUCPR+ dataset [10] is the subset of PKLot [3], which is annotated with nearly 17, 000 cars in total.”
• Parking-stall vacancy indicator system, based on deep convolutional neural networks (2016, methodology): “Following the same procedure of the authors in [5], each set is split into 50% training and 50% testing.”