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RT-MDNet: Real-Time Multi-Domain Convolutional Neural Network Tracker

Created by Ilchae Jung, Jeany Son, Mooyeol Baek, and Bohyung Han

Introduction

RT-MDNet is the real-time extension of MDNet and is the state-of-the-art real-time tracker. Detailed description of the system is provided by our project page and paper

Citation

If you're using this code in a publication, please cite our paper.

@InProceedings{rtmdnet,
author = {Jung, Ilchae and Son, Jeany and Baek, Mooyeol and Han, Bohyung},
title = {Real-Time MDNet},
booktitle = {European Conference on Computer Vision (ECCV)},
month = {Sept},
year = {2018}
}

Notice

We re-write the implementation of the roi_align to support the high version pytorch and now this code supports pytorch 1.0+.

How to use

You need to complie the roi_align first.

Demo

  1. go to the dir of the roi_align
  2. run python setup.py build_ext --inplace

System Requirements

This code is tested on 64 bit Linux (Ubuntu 16.04 LTS).

Prerequisites

Online Tracking

Pretrained Model and results If you only run the tracker, you can use the pretrained model: RT-MDNet-ImageNet-pretrained. Also, results from pretrained model are provided in here.

Demo 0. Run 'Run.py'.

Learning RT-MDNet

Preparing Datasets

  1. If you download ImageNet-Vid dataset, you run 'modules/prepro_data_imagenet.py' to parse meta-data from dataset. After that, 'imagenet_refine.pkl' is generized.
  2. type the path of 'imagenet_refine.pkl' in 'train_mrcnn.py'

Demo