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Code to reproduce 'Deep Learning for Decentralized Parking Lot Occupancy Detection' paper.
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pyffe @ be98c54 updated pyffe submodule Apr 23, 2019
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README.md Create README.md Jan 26, 2018
main.py added main experiments Jan 26, 2018
pklot.py added main experiments Jan 26, 2018

README.md

Deep Learning for Decentralized Parking Lot Occupancy Detection

This repo contains code to reproduce the experiments presented in Deep Learning for Decentralized Parking Lot Occupancy Detection.

Visit the project website for more info and resources (dataset, pre-trained models).

Requirements

  • Caffe with Python interface (PyCaffe)

Steps to reproduce experiments

  1. Clone this repo together with its submodules:

    git clone --recursive https://github.com/fabiocarrara/deep-parking.git
  2. Download the datasets using the following links and extract them somewhere.

    Dataset Link Size
    CNRPark http://cnrpark.it/dataset/CNRPark-Patches-150x150.zip 36.6 MB
    CNR-EXT http://cnrpark.it/dataset/CNR-EXT-Patches-150x150.zip 449.5 MB
    PKLot visit PKLot webpage 4.6 GB
  3. Get the dataset splits and extract them in the repo folder

    # Listfile containing dataset splits
    wget http://cnrpark.it/dataset/splits.zip
    unzip splits.zip
  4. Add a config.py files inside each folder in splits/ to tell pyffe where the images are. The content of the files should be like this (adjust the root_dir attribute to the absolute path of the extracted datasets):

    config = dict(root_folder = '/path/to/dataset/dir/')

    This path will be prepended to each line in the list files defining the various splits.

  5. Train and evaluate all the models by running:

    python main.py

    Modify main.py to select the experiments you want to reproduce. Run pklot.py if you want to train and evaluate our architecture on the PKLot splits only.

Citation

@article{amato2017deep,
  title={Deep learning for decentralized parking lot occupancy detection},
  author={Amato, Giuseppe and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Meghini, Carlo and Vairo, Claudio},
  journal={Expert Systems with Applications},
  volume={72},
  pages={327--334},
  year={2017},
  publisher={Pergamon}
}
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