Awesome
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
- go to the dir of the roi_align
- run python setup.py build_ext --inplace
System Requirements
This code is tested on 64 bit Linux (Ubuntu 16.04 LTS).
Prerequisites
- Python3
- PyTorch (>= 0.4.0)
- For GPU support, a GPU (~2GB memory for test) and CUDA toolkit.
- Training Dataset (ImageNet-Vid) if needed.
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
- 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.
- type the path of 'imagenet_refine.pkl' in 'train_mrcnn.py'
Demo
- Run 'train_mrcnn.py' after hyper-parameter tuning suitable to the capacity of your system.