Installation

1. Clone and enter this repository:

   git clone git@github.com:timmeinhardt/trackformer.git
   cd trackformer
   

2. Install packages for Python 3.7:

   1. pip3 install -r requirements.txt
   2. Install PyTorch 1.5 and torchvision 0.6 from here.
   3. Install pycocotools (with fixed ignore flag): pip3 install -U 'git+https://github.com/timmeinhardt/cocoapi.git#subdirectory=PythonAPI'
   4. Install MultiScaleDeformableAttention package: python src/trackformer/models/ops/setup.py build --build-base=src/trackformer/models/ops/ install

3. Download and unpack datasets in the data directory:

   1. MOT17:

      wget https://motchallenge.net/data/MOT17.zip
      unzip MOT17.zip
      python src/generate_coco_from_mot.py
      

   2. (Optional) MOT20:

      wget https://motchallenge.net/data/MOT20.zip
      unzip MOT20.zip
      python src/generate_coco_from_mot.py --mot20
      

   3. (Optional) MOTS20:

      wget https://motchallenge.net/data/MOTS.zip
      unzip MOTS.zip
      python src/generate_coco_from_mot.py --mots
      

   4. (Optional) CrowdHuman:

      1. Create a CrowdHuman and CrowdHuman/annotations directory.
      2. Download and extract the train and val datasets including their corresponding *.odgt annotation file into the CrowdHuman directory.
      3. Create a CrowdHuman/train_val directory and merge or symlink the train and val image folders.
      4. Run python src/generate_coco_from_crowdhuman.py
      5. The final folder structure should resemble this:

         |-- data
             |-- CrowdHuman
             |   |-- train
             |   |   |-- *.jpg
             |   |-- val
             |   |   |-- *.jpg
             |   |-- train_val
             |   |   |-- *.jpg
             |   |-- annotations
             |   |   |-- annotation_train.odgt
             |   |   |-- annotation_val.odgt
             |   |   |-- train_val.json
         

4. Download and unpack pretrained TrackFormer model files in the models directory:

   wget https://vision.in.tum.de/webshare/u/meinhard/trackformer_models_v1.zip
   unzip trackformer_models_v1.zip
   

5. (optional) The evaluation of MOTS20 metrics requires two steps:

   1. Run Trackformer with src/track.py and output prediction files
   2. Download the official MOTChallenge devkit and run the MOTS evaluation on the prediction files

In order to configure, log and reproduce our computational experiments, we structure our code with the Sacred framework. For a detailed explanation of the Sacred interface please read its documentation.