Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features

Martin Ahrnbom; Karl Åström; Mikael Nilsson

doi:10.1109/CVPRW.2016.200

Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features

Martin Ahrnbom, Karl Åström, Mikael Nilsson

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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.

Original language	English
Title of host publication	Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
Publisher	IEEE Computer Society
Pages	1609-1615
Number of pages	7
ISBN (Electronic)	9781467388504
DOIs	https://doi.org/10.1109/CVPRW.2016.200
Publication status	Published - 2016 Dec 16
Event	2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States Duration: 2016 Jun 26 → 2016 Jul 1

Conference

Conference	2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Country/Territory	United States
City	Las Vegas
Period	2016/06/26 → 2016/07/01

Subject classification (UKÄ)

Mathematical Sciences
Computer graphics and computer vision

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10.1109/CVPRW.2016.200

http://www.cv-foundation.org//openaccess/content_cvpr_2016_workshops/w25/papers/Ahrnbom_Fast_Classification_of_CVPR_2016_paper.pdf

Cite this

@inproceedings{3c98a5ce4f2b4ae8af775767e050bdfe,

title = "Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features",

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 = "Martin Ahrnbom and Karl {\AA}str{\"o}m and Mikael Nilsson",

year = "2016",

month = dec,

day = "16",

doi = "10.1109/CVPRW.2016.200",

language = "English",

pages = "1609--1615",

booktitle = "Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016",

publisher = "IEEE Computer Society",

address = "United States",

note = "2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016",

}

Ahrnbom, M, Åström, K & Nilsson, M 2016, Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features. in Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016., 7789690, IEEE Computer Society, pp. 1609-1615, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, United States, 2016/06/26. https://doi.org/10.1109/CVPRW.2016.200

Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features. / Ahrnbom, Martin; Åström, Karl ; Nilsson, Mikael.
Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016. IEEE Computer Society, 2016. p. 1609-1615 7789690.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

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AU - Nilsson, Mikael

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N2 - 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.

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BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016

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ER -

Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features

Abstract

Conference

Subject classification (UKÄ)

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