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MOSiamFC - TensorFlow

Forked version of SiamFC that supports Multi Object Tracking. Also this version is made to be compatible with Python 3.x .

Note1: Tested in Windows platform, using the Anaconda Platform.

Note2: This fork also applied the pull request to use OpenCV to show the frame results.

For more information, please refer to the original Repository.

Settings things up with virtualenv

  1. Get virtualenv if you don't have it already pip install virtualenv
  2. Create new virtualenv with Python 3.6 virtualenv --python=/usr/bin/python3.6 mo-siam
  3. Activate the virtualenv source ~/mo-siam/bin/activate
  4. Clone the repository git clone https://github.com/lukaswals/siamfc-tf.git
  5. cd siamfc-tf
  6. Install the required packages sudo pip install -r requirements.txt
  7. mkdir pretrained data
  8. Download the pretrained networks in pretrained and unzip the archive (we will only use baseline-conv5_e55.mat)
  9. Download video sequences in data and unzip the archive.

Bounding Box Input

It's important to note that the provided Ground Truth for the video sequences are only for one object.

Running the tracker

  1. Set video from parameters.evaluation to "all" or to a specific sequence (e.g. "vot2016_ball1")
  2. See if you are happy with the default parameters in parameters/hyperparameters.json
  3. Enable Multi-Object tracking by setting multi_object from parameters.evaluation to 1 (default value)
  4. Optionally enable visualization in parameters/run.json
  5. Call the main script (within an active virtualenv session) python run_tracker_evaluation.py

Fork Authors

  • Lucas Wals

Original Authors

References

If you find their work useful, please consider citing

↓ [Original method] ↓

@inproceedings{bertinetto2016fully,
  title={Fully-Convolutional Siamese Networks for Object Tracking},
  author={Bertinetto, Luca and Valmadre, Jack and Henriques, Jo{\~a}o F and Vedaldi, Andrea and Torr, Philip H S},
  booktitle={ECCV 2016 Workshops},
  pages={850--865},
  year={2016}
}

↓ [Improved method and evaluation] ↓

@article{valmadre2017end,
  title={End-to-end representation learning for Correlation Filter based tracking},
  author={Valmadre, Jack and Bertinetto, Luca and Henriques, Jo{\~a}o F and Vedaldi, Andrea and Torr, Philip HS},
  journal={arXiv preprint arXiv:1704.06036},
  year={2017}
}

License

This code can be freely used for personal, academic, or educational purposes.