Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features
Ahrnbom, Martin LU
; Åström, Karl
LU
and Nilsson, Mikael
LU
(2016)
2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
p.1609-1615
- Abstract
- In this paper we present a novel, fast and accurate system for detecting the presence of cars in parking lots. The system is based on fast integral channel features and machine learning. The methods are well suited for running embedded on low performance platforms. The methods are tested on a database of nearly 700,000 images of parking spaces, where 48.5% are occupied and the rest are free. The experimental evaluation shows improved robustness in comparison to the baseline methods for the dataset.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3c98a5ce-4f2b-4ae8-af77-5767e050bdfe
- author
- Ahrnbom, Martin
LU
; Åström, Karl
LU
and Nilsson, Mikael
LU
- organization
- publishing date
- 2016-12-16
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
- article number
- 7789690
- pages
- 7 pages
- publisher
- IEEE Computer Society
- conference name
- 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
- conference location
- Las Vegas, United States
- conference dates
- 2016-06-26 - 2016-07-01
- external identifiers
-
- wos:000391572100193
- scopus:85010189034
- ISBN
- 9781467388504
- DOI
- 10.1109/CVPRW.2016.200
- language
- English
- LU publication?
- yes
- id
- 3c98a5ce-4f2b-4ae8-af77-5767e050bdfe
- alternative location
- http://www.cv-foundation.org//openaccess/content_cvpr_2016_workshops/w25/papers/Ahrnbom_Fast_Classification_of_CVPR_2016_paper.pdf
- date added to LUP
- 2016-10-27 11:21:51
- date last changed
- 2024-04-05 08:37:40
@inproceedings{3c98a5ce-4f2b-4ae8-af77-5767e050bdfe,
abstract = {{In this paper we present a novel, fast and accurate system for detecting the presence of cars in parking lots. The system is based on fast integral channel features and machine learning. The methods are well suited for running embedded on low performance platforms. The methods are tested on a database of nearly 700,000 images of parking spaces, where 48.5% are occupied and the rest are free. The experimental evaluation shows improved robustness in comparison to the baseline methods for the dataset.}},
author = {{Ahrnbom, Martin and Åström, Karl and Nilsson, Mikael}},
booktitle = {{Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016}},
isbn = {{9781467388504}},
language = {{eng}},
month = {{12}},
pages = {{1609--1615}},
publisher = {{IEEE Computer Society}},
title = {{Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features}},
url = {{http://dx.doi.org/10.1109/CVPRW.2016.200}},
doi = {{10.1109/CVPRW.2016.200}},
year = {{2016}},
}