HM-Net_WAMI

@article{HMNet,
  title={HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery},
  author={Motorcu, Hakki and Ates, Hasan F. and Ugurdag, H. Fatih and Gunturk, Bahadir K.},
  journal={IEEE Access},
  year={2022},
  doi={10.1109/ACCESS.2021.3138980}
}

Installation

HM-Net_ROOT=/path/to/clone/HM-Net_ROOT
git clone https://github.com/HakkiMotorcu/HM-Net_WAMI $HM-Net_ROOT

cd HM-Net_ROOT
conda create --name HM_Net python=3.9.12
conda activate HM_Net
conda install pytorch torchvision cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

Data Format

{HM-Net_ROOT}
 |-- Datasets
 `-- |-- WPAFB
     `-- |--- train
         |    |--- annotations (txt files named as {track_name}.txt  
         |    |--- sequences (folders named after {track_name} contains images 
         |    |--- train.json (coco format annotations can be generated)
         |--- test
         |   |--- annotations
         |   |--- ...

To generate json files, we provided one example converter under "Source/lib.data/data_tools/" "sat2coco.py", which takes the format below and converts it to coco.

<frame_index>,<target_id>,<bbox_left>,<bbox_top>,<bbox_width>,<bbox_height>,<score>,<object_category>,<truncation>,<occlusion>

Important Note: All information related to a dataset is kept under "Source/data_conf/" folder. After preparing dataset create a json file (template file can be found on the folder).

Note: In paper original all used datasets were motion stabilizled datasets.

Usage

For training and testing purposes consult to "Source.lib.setup.py" and given ".sh" files.

Acknowledgement

While writing this repo, we were inspired by the following ifzhang/FairMOT and xingyizhou/CenterTrack repositories.