Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Visual Tracking via Dynamic Memory Networks

Introduction

This is the Tensorflow implementation of our MemDTC tracker published in TPAMI, 2019. It extends our MemTrack by proposing a Distractor Template Canceling mechanism. Detailed comparision results can be found in the author's webpage

Prerequisites

  • Python 3.5 or higher
  • Tensorflow 1.2.1 or higher
  • CUDA 8.0

Path setting

Set proper home_path in config.py accordingly in order to proceed the following step. Make sure that you place the tracking data properly according to your path setting.

Tracking Demo

You can use our pretrained model to test our tracker first.

  1. Download the model from the link: GoogleDrive
  2. Put the model into directory ./output/models
  3. Run python3 demo.py in directory ./tracking

Training

  1. Download the ILSRVC data from the official website and extract it to proper place according to the path in config.py.
  2. Then run the sh process_data.sh in ./build_tfrecords directory to convert ILSVRC data to tfrecords.
  3. Run python3 experiment.py to train the model.

Citing MemTrack

If you find the code is helpful, please cite

@article{Yang2019pami,
	author = {Yang, Tianyu and Chan, Antoni B.},
	journal = {TPAMI},
	title = {{Visual Tracking via Dynamic Memory Networks}},
	year = {2019}
}

About

Code for "Visual Tracking via Dynamic Memory Networks"

Resources

License

Releases

No releases published

Packages

No packages published