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).

• Caffe with Python interface (PyCaffe)

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.

@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}
}