FairMOT

FairMOT

Requirements

• Clone this repo, and we'll call the DIVOTrack_github/Single_view_Tracking/FairMOT/ as ${ROOT}
• Install dependencies. We use python 3.8 and pytorch >= 1.7.0

conda create -n fairmot
conda activate fairmot
conda install pytorch==1.7.0 torchvision==0.8.1 cudatoolkit=11.0 -c pytorch
cd ./Training_Detector
pip install cython
pip install -r requirements.txt

• We use DCNv2_pytorch_1.7 in our backbone network (pytorch_1.7 branch). Previous versions can be found in DCNv2.

git clone -b pytorch_1.7 https://github.com/ifzhang/DCNv2.git
cd DCNv2
./make.sh

Data preparation

The data in the following structure:

DIVOTrack_github
    └——————datasets
    |        └——————DIVO
    |           |——————images
    |           |        └——————train
    |           |        └——————test
    |           └——————labels_with_ids
    |                    └——————train
    └——————${ROOT}

Pretrained model

• You can download the pretrained model from Google Drive. After downloading, you should put the pretrained models in the following structure:

${ROOT}
   └——————models
           └——————fairmot_dla34.pth

Training

• Download the training data
• To train the model in the paper, run this command:

sh experiments/train.sh

Inference

• To get the inference results, run:

sh experiments/test.sh

• The result with be save to ../result/exp_id/

Evaluation

1. Change the directory name from "result_divo" to "fairmot"
2. Make sure "centertrack" has the middle directory "data". (i.e. fairmot/data/circleRegion_Drone.txt instead of fairmot/circleRegion_Drone.txt)
3. Resize the result files by "resize.py"
4. Copy your result_divo to DIVOTrack/TrackEval/data/trackers/mot_challenge/divo
5. Go to DIVOTrack/TrackEval
6. See the instruction on TrackEval

Final Model

You can download our final model here: FairMOT model

After downloading, you should put the final detection model in the following structure:

${ROOT}
   └——————exp
           └——————mot
                   └——————train_det
                            └——————model_det_last.pth