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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 In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 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:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
pages
7 pages
publisher
IEEE Computer Society
conference name
2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
external identifiers
  • scopus:85010189034
  • wos:000391572100193
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
2017-11-14 09:51:28
@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},
  year         = {2016},
}