Tracker

MLP-MHCA-VOT

Models (./workSpace/checkpoints/ltr/mlp-mhca/mlp-mhca) Baidu(votf)

Raw Results(./results)

Installation

This document contains detailed instructions for installing the necessary dependencied for MLP-MHCA. The instructions have been tested on Ubuntu 18.04 system.

Install dependencies

• Create and activate a conda environment

  conda create -n mlp-mhca python=3.7
  conda activate mlp-mhca

• Install PyTorch

  conda install -c pytorch pytorch=1.5 torchvision=0.6.1 cudatoolkit=10.2

• Install other packages

  conda install matplotlib pandas tqdm
  pip install opencv-python tb-nightly visdom scikit-image tikzplotlib gdown
  conda install cython scipy
  sudo apt-get install libturbojpeg
  pip install pycocotools jpeg4py
  pip install wget yacs
  pip install shapely==1.6.4.post2

• Setup the environment
  Create the default environment setting files.

  # Change directory to <PATH_of_MLP-MHCA>
  cd MLP-MHCA-VOT
  
  # Environment settings for pytracking. Saved at pytracking/evaluation/local.py
  python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()"
  
  # Environment settings for ltr. Saved at ltr/admin/local.py
  python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()"

You can modify these files to set the paths to datasets, results paths etc.

• Add the project path to environment variables
  Open ~/.bashrc, and add the following line to the end. Note to change <path_of_MLP-MHCA> to your real path.

  export PYTHONPATH=<path_of_MLP-MHCA>:$PYTHONPATH
  

Quick Start

Traning

• Modify local.py to set the paths to datasets, results paths etc.
• Runing the following commands to train the MLP-MHCA. You can customize some parameters by modifying mlp_mhca.py

  conda activate mlp-mhca
  cd MLP-MHCA-VOT/ltr
  python run_training.py mlp-mhca mlp_mhca

Evaluation

• We integrated PySOT for evaluation. You can download json files in PySOT

  You need to specify the path of the model and dataset in the test.py.

  net_path = '/path_to_model' #Absolute path of the model
  dataset_root= '/path_to_datasets' #Absolute path of the datasets

  Then run the following commands.

  conda activate mlp-mhca
  cd MLP-MHCA-VOT
  python -u pysot_toolkit/test.py --dataset <name of dataset> --name 'mlp-mhca' #test tracker 
  python pysot_toolkit/eval.py --tracker_path results/ --dataset <name of dataset> --num 1 --tracker_prefix 'mlp-mhca' #eval tracker

  The testing results will in the current directory(./results/dataset/mlp-mhca/)

Tune

• For the most suitable hyperparameters for the tracker, we provide script to seach automatically

  conda activate mlp-mhca
  cd MLP-MHCA-VOT/pysot_toolkit
  python tune.py --dataset <name of dataset>

  The tuned results will in the current directory(./pysot_toolkit/tune_results)

• You can also use pytracking to test and evaluate tracker. The results might be slightly different with PySOT due to the slight difference in implementation (pytracking saves results as integers, pysot toolkit saves the results as decimals).

This is a modified version of the python framework TransT based on Pytorch, also borrowing from PySOT. We would like to thank their authors for providing great frameworks and toolkits.

Contact

• Sun Shipeng (email:983082671@qq.com)