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tangyuhao
126 Stars 63 Forks MIT License 25 Commits 23 Opened issues

Description

A tensorflow implementation for GOTURN tracker

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# 327,154
Python
24 commits

GOTURN-Tensorflow

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.

Environment

  • 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:
      train/target/000024.jpg,train/searching/000024.jpg,0.29269230769230764,0.22233115468409587,0.7991794871794871,0.7608061002178649
  3. Change related places in
    train.py
  4. Train it and wait!
    python
    python train.py
    
  5. The log file is
    train.log
    by default

Test

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

TIPS

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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