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

Martin Ahrnbom, Kalle Åström, Mikael G. Nilsson

CVPRW 2016 pp. 1609-1615

doi:10.1109/CVPRW.2016.200 /cvprw/2016/ahrnbom2016cvprw-fast/

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.

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Cite

Text

Ahrnbom et al. "Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016. doi:10.1109/CVPRW.2016.200

Markdown

[Ahrnbom et al. "Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016.](https://mlanthology.org/cvprw/2016/ahrnbom2016cvprw-fast/) doi:10.1109/CVPRW.2016.200

BibTeX

@inproceedings{ahrnbom2016cvprw-fast,
  title     = {{Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features}},
  author    = {Ahrnbom, Martin and Åström, Kalle and Nilsson, Mikael G.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2016},
  pages     = {1609-1615},
  doi       = {10.1109/CVPRW.2016.200},
  url       = {https://mlanthology.org/cvprw/2016/ahrnbom2016cvprw-fast/}
}