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https://github.com/wuyenlin/parking_lot_occupancy_detection
Parking Lot Occupancy Detection in PyTorch (http://cnrpark.it)
https://github.com/wuyenlin/parking_lot_occupancy_detection
Last synced: 6 days ago
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Parking Lot Occupancy Detection in PyTorch (http://cnrpark.it)
- Host: GitHub
- URL: https://github.com/wuyenlin/parking_lot_occupancy_detection
- Owner: wuyenlin
- License: agpl-3.0
- Created: 2020-06-17T10:02:09.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-01-11T10:36:05.000Z (almost 3 years ago)
- Last Synced: 2023-10-20T23:51:33.347Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 732 KB
- Stars: 16
- Watchers: 3
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Parking Lot Occupancy Detection
[](https://colab.research.google.com/drive/19WoAA0vinucKOj-dxMs8je-tKms8rZza?usp=sharing)
This repository contains the code to reproduce the result of [Deep learning for decentralized parking lot occupancy detection](https://www.sciencedirect.com/science/article/abs/pii/S095741741630598X).
More details regarding the paper can be found on [CNRPark+EXT](http://cnrpark.it/), where dataset and labels could be downloaded.
This reproduction code is done by Hao Liu, Sigurd Totland, and Yen-Lin Wu.
### Download dataset
There are 3 sets of dataset and their labels required to run this code. Run [get_dataset.sh](get_dataset.sh) as follows:
```
bash get_dataset.sh
```
This command will download the datasets and unzip them in the project root directory.
Make sure to include the correct directory in the next section when parsing the argument.
### Running the code
Run the code as follows:
```
python3 main.py
```
By default, it runs `epochs=18`, train on `CNRPark Even` and test on `CNRPark Odd`.
If a trained model is to be loaded and test on other dataset (i.e. `.pth` file exists), or AlexNet is to be used, run the following command:
```
python3 main.py --path trained_model/sunny.pth --model AlexNet
```
See arguments in [options.py](utils/option.py).
### Requirements
```
python >= 3.6
pytorch >= 0.4
```
### Results
For the moment, only Table 2 and Figure 5 are reproduced from the paper. Some variances could be observed from the results compared to paper. The optimal epochs for each experiment are still being worked on.
Results of Table 2 are shown below, with epochs=18.
|Test set | Paper | Pytorch |
|----- |----- | ----- |
|Trained on UFPR04 |
|UFPR04 | 0.9954| 0.9600 |
|UFPR05 | 0.9329| 0.7990 |
|PUC | 0.9827| 0.9300 |
|Trained on UFPR05 |
|UFPR04 | 0.9369| 0.8000 |
|UFPR05 | 0.9949| 0.9760 |
|PUC | 0.9272| 0.9010 |
|Trained on PUC |
|UFPR04 | 0.9803| 0.9560 |
|UFPR05 | 0.9600| 0.9490 |
|PUC | 0.9990| 0.9890 |
|Trained on CNRParkOdd
|CNRParkEven|0.9013|0.9190 |
|Trained on CNRParkEven
|CNRParkOdd|0.9071| 0.9240 |
Results of Figure 5 are shown below.
Paper results:
|Test set | Paper | Pytorch |
|----- |----- | ----- |
|Trained on SUNNY |
|OVERCAST | 0.970 | 0.946 |
|RAINY | 0.960 | 0.912 |
|PKLot | 0.850 | 0.759 |
|Trained on OVERCAST |
|SUNNY | 0.920 | 0.917 |
|RAINY | 0.950 | 0.920 |
|PKLot | 0.820 | 0.709 |
|Trained on RAINY |
|SUNNY | 0.940 | 0.914 |
|OVERCAST | 0.970 | 0.959 |
|PKLot | 0.920 | 0.651 |
### References
```
@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}
}
```