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A tensorflow implementation for GOTURN tracker

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This is a tensorflow implementation of GOTURN.

Thanks to author David Held for his help of this implementation.

The original paper is:

Learning to Track at 100 FPS with Deep Regression Networks,
David Held, Sebastian Thrun, Silvio Savarese,

The github repo for caffe implementation is given by the authors: davheld/GOTURN

Brief illustration of how this network works:

You can refer to the paper or github repo above for more details.


  • python3
  • tensorflow 1.0+, both cpu and gpu work fine

How to use it

Finetune for your own dataset

  1. Create a folder, fill in all training images
  2. Create a .txt file
    • It should contains target image, searching image and ground-truth bounding box
    • Bounding box is in the form of
      , usually from 0 to 1, but exceeding this range is also fine.
    • Example of one line:
  3. Change related places in
  4. Train it and wait!
  5. The log file is
    by default


  1. Download pretrained model from: GOTURN_MODEL
  2. Uncompress the
    folder, and put it in the root directory of this repo
  3. Test on examples just by running
  4. The log file is
    by default


Be careful, the output of this network actually always from 0 to 10 thus I multiplied the ground-truth bounding boxes( always ranging from 0 to 1) by 10.

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