/Aerial_Vehicle_Classification

Tensorflow Implementation of PixelRNN and CNN based Aerial Vehicle Classification (DeepHKCF) - [IEEE-TGRS19]

Primary LanguagePython

Aerial Vehicle Detection using Synthetic Images

This repository contains our work on Aerial Vehicle Detection using Synthetic Images. You can find more details on our work in the following paper :

@article{uzkent2017tracking,
	Title={Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters},
	Author={Uzkent, Burak and Rangnekar, Aneesh and Hoffman, Matthew J},
	Journal={arXiv preprint arXiv:1711.07235},
	Year={2017}
}

Synthetic Training Dataset

The goal of our work is detect vehicles on the WAMI platform by using convolutional model trained on a synthetic vehicle detection dataset. Our synthetic dataset consists of 55226 images with vehicle and background samples and each image is represented by 48x48 pixels. The synthetic images are generated by the Digital Imaging and Remote Sensing Software (DIRSIG) owned by the Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology. The synthetic scenes comes from the MegaScene-I area available in DIRSIG.The vehicular traffic simulation, on ther other hand, is generated by the Simulation of Urban Mobility Platform (SUMO) and coupled to DIRSIG. Some of the positive samples from our dataset is shown below.

DIRSIG_Positives

Validation Dataset

To test the fidelity of the synthetic dataset, we validate the performance of the convolutional network, trained on the synthetic dataset, on the validation dataset containing the real vehicle and background images recorded with the WAMI platform. Our validation dataset consists of 600 images from the CLIFF06 and CLIFF07 datasets. Some of the positive images from the validation dataset can be visualized in the figure below.

WAMI_Positives

The goal of this study is to reduce the dependency on the WAMI dataset to train a convolutional network model. With our synthetic dataset, we believe that the overfitting to the a couple of datasets captured from the WAMI dataset is avoided.

Download Links

Download our aerial vehicle detection dataset

wget https://drive.google.com/open?id=1cQIM2a7gNaxlE2oFdQ_O-GqgBo84fLia

If you use our aerial vehicle detection dataset in your research project, please cite our paper :

@article{uzkent2017tracking,
	Title={Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters},
	Author={Uzkent, Burak and Rangnekar, Aneesh and Hoffman, Matthew J},
	Journal={arXiv preprint arXiv:1711.07235},
	Year={2017}
}

Train ResNet50 Model on the DIRSIG Vehicle Classification Dataset

In this step, the DIRSIG dataset is used to train the tensorflow implementation of the ResNet50 network. To train the model, first download the dataset as explained in the steps above. For the next step, please visit the following README file.