by Hyeonseob Nam and Bohyung Han at POSTECH

Update (April, 2019)

• Migration to python 3.6 & pyTorch 1.0
• Efficiency improvement (~5fps)
• ImagNet-VID pretraining
• Code refactoring

PyTorch implementation of MDNet, which runs at ~5fps with a single CPU core and a single GPU (GTX 1080 Ti).

If you're using this code for your research, 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}
}

• Raw results of MDNet pretrained on VOT-OTB (VOT13,14,15 excluding OTB): Google drive link
• Raw results of MDNet pretrained on Imagenet-VID: Google drive link

• python 3.6+
• opencv 3.0+
• PyTorch 1.0+ and its dependencies
• for GPU support: a GPU with ~3G memory

 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]

• 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