py-VITAL

by Xiaoping Wang.

Introduction

Python (PyTorch) implementation of VITAL tracker. VITAL is a great tracker invented by Song, Yibing and Ma, Chao and et al. This implementation is based on py-MDNet, it is implemented by Hyeonseob Nam and Bohyung Han. Thanks to all of them.

[Project] [Paper] [Matlab code]

If you want this code for personal use, please cite:

@InProceedings{nam2016mdnet,
author = {Nam, Hyeonseob and Han, Bohyung},
title = {Learning Multi-Domain Convolutional Neural Networks for Visual Tracking},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}  

@inproceedings{song-cvpr18-VITAL,
author = {Song, Yibing and Ma, Chao and Wu, Xiaohe and Gong, Lijun and Bao, Linchao and Zuo, Wangmeng and Shen, Chunhua and Lau, Rynson and Yang, Ming-Hsuan}, 
title = {VITAL: VIsual Tracking via Adversarial Learning}, 
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},    
year = {2018},
}  
  
@inproceedings{xpwang-VITAL-PyTorch,
author = {Xiaoping Wang}, 
title = {VITAL: VIsual Tracking via Adversarial Learning}, 
booktitle = {VITAL tracker implemented by PyTorch}, 
month = {March},
year = {2019},
}  

Prerequisites

• python 3.6+
• opencv 3.0+
• PyTorch 1.0+ and its dependencies

Usage

Tracking

 python tracking/run_tracker.py -s DragonBaby [-d (display fig)] [-f (save fig)]

• You can provide a sequence configuration in two ways (see tracking/gen_config.py):
  • python tracking/run_tracker.py -s [seq name]
  • python tracking/run_tracker.py -j [json path]

Pretraining

• Download VGG-M (matconvnet model) and save as "models/imagenet-vgg-m.mat"
• Pretraining on VOT-OTB
  • Download VOT datasets into "datasets/VOT/vot201x"

   python pretrain/prepro_vot.py
   python pretrain/train_mdnet.py -d vot

• Pretraining on ImageNet-VID
  • Download ImageNet-VID dataset into "datasets/ILSVRC"

   python pretrain/prepro_imagenet.py
   python pretrain/train_mdnet.py -d imagenet