https://github.com/fabiocarrara/deep-parking
Code to reproduce 'Deep Learning for Decentralized Parking Lot Occupancy Detection' paper.
https://github.com/fabiocarrara/deep-parking
caffe cnn-classification image-classification parking pycaffe python2
Last synced: 11 months ago
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Code to reproduce 'Deep Learning for Decentralized Parking Lot Occupancy Detection' paper.
- Host: GitHub
- URL: https://github.com/fabiocarrara/deep-parking
- Owner: fabiocarrara
- Created: 2018-01-26T15:29:08.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-08-30T07:24:23.000Z (almost 3 years ago)
- Last Synced: 2024-11-27T04:30:44.629Z (over 1 year ago)
- Topics: caffe, cnn-classification, image-classification, parking, pycaffe, python2
- Language: Python
- Homepage: http://cnrpark.it
- Size: 18 MB
- Stars: 99
- Watchers: 10
- Forks: 45
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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](https://www.sciencedirect.com/science/article/pii/S095741741630598X).
Visit the [project website](http://cnrpark.it/) 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:
```bash
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](https://web.inf.ufpr.br/vri/databases/parking-lot-database/) | 4.6 GB |
3. Get the dataset splits and extract them in the repo folder
```bash
# 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):
```python
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:
```bash
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}
}
```