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SiamFC tracking in TensorFlow.

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SiamFC - TensorFlow

TensorFlow port of the tracking method described in the paper Fully-Convolutional Siamese nets for object tracking.

In particular, it is the improved version presented as baseline in End-to-end representation learning for Correlation Filter based tracking, which achieves state-of-the-art performance at high framerate. The other methods presented in the paper (similar performance, shallower network) haven't been ported yet.

Note1: results should be similar (i.e. slightly better or worse) than our MatConvNet implementation. However, for direct comparison please refer to the precomputed results available in the project pages or to the original code, which you can find pinned in my GitHub.

Note2: at the moment this code only allows to use a pretrained net in forward mode.

Settings things up with virtualenv

1) Get virtualenv if you don't have it already

pip install virtualenv
1) Create new virtualenv with Python 2.7
virtualenv --python=/usr/bin/python2.7 ve-tracking
1) Activate the virtualenv
source ~/tracking-ve/bin/activate
1) Clone the repository
git clone
cd siamfc-tf
1) Install the required packages
sudo pip install -r requirements.txt
mkdir pretrained data
1) Download the pretrained networks in
and unzip the archive (we will only use
) 1) Download video sequences in
and unzip the archive.

Running the tracker

1) Set

or to a specific sequence (e.g.
) 1) See if you are happy with the default parameters in
1) Optionally enable visualization in
1) Call the main script (within an active virtualenv session)



If you find our work useful, please consider citing

↓ [Original method] ↓

  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},
↓ [Improved method and evaluation] ↓
  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},


This code can be freely used for personal, academic, or educational purposes. Please contact us for commercial use.

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