Parking Lot Occupancy Detection

This repository contains the code to reproduce the result of Deep learning for decentralized parking lot occupancy detection. More details regarding the paper can be found on CNRPark+EXT, 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 as follows:

sudo chmod +x get_dataset.sh
./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.

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