skyoung / Memtrack
Licence: mit
Code for "Learning Dynamic Memory Networks for Object Tracking"
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Learning Dynamic Memory Networks for Object Tracking
We extend our MemTrack with Distractor Template Canceling mechamism in our journal verison, please check our new method MemDTC. Code is availabe at MemDTC-code
Introduction
This is the Tensorflow implementation of our MemTrack tracker published in ECCV, 2018. 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.
- Download the model from the link: GoogleDrive
- Put the model into directory
./output/models
- Run
python3 demo.py
in directory./tracking
Training
- Download the ILSRVC data from the official website and extract it to proper place according to the path in
config.py
. - Then run the
sh process_data.sh
in./build_tfrecords
directory to convert ILSVRC data to tfrecords. - Run
python3 experiment.py
to train the model.
Citing MemTrack
If you find the code is helpful, please cite
@inproceedings{Yang2018,
author = {Yang, Tianyu and Chan, Antoni B.},
booktitle = {ECCV},
title = {{Learning Dynamic Memory Networks for Object
Tracking}},
year = {2018}
}
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