For VOT21 challenge model weight download:

Download model weight

We provide the models of Five trackers SAMN, SAMN_DiMP, DualTFR, DualTFRst, DualTFRon here.

Note that the AlphaRefine (https://github.com/MasterBin-IIAU/AlphaRefine) model and SuperDiMP (https://github.com/visionml/pytracking) model are the same with the original author.

Tracker	model quantity	model name
SAMN	1	SAMN.tar
SAMN_DiMP	2	super_dimp.pth.tar, SAMN.tar
DualTFR	2	DualTFR.tar, ar.pth.tar
DualTFRst	2	DualTFRst.tar, ar.pth.tar
DualTFRon	2	DualTFRon.tar, ar.pth.tar

Models can be downloaded from BaiduNetDisk or GoogleDrive:

BaiduNetDisk:

https://pan.baidu.com/s/1RHA7HVlXtNEzYPGIjJbQ-g (sruh)

GoogleDrive:

https://drive.google.com/drive/folders/1Z61_mfh2vwzqDxejt5idBOgYhWOCZOr5?usp=sharing

Discriminative Segmentation Tracking Using Dual Memory Banks ---> Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking

Python (PyTorch) implementation of the DMB tracker.

Released in Arxiv.:

Fei Xie, Wankou Yang, Bo Liu, Kaihua Zhang, Wanli Xue, Wangmeng Zuo.

Discriminative Segmentation Tracking Using Dual Memory Banks

Paper

Notification

The original paper will be reproduced as : Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Full version of code will be available after this method is publicily published.

Summary of the DMB tracker

Existing template-based trackers usually localize the target in each frame with bounding box, thereby being limited in learning pixel-wise representation and handling complex and non-rigid transformation of the target. Further, existing segmentation tracking methods are still insufficient in modeling and exploiting dense correspondence of target pixels across frames. To overcome these limitations, this work presents a novel discriminative segmentation tracking architecture equipped with dual memory banks, i.e., appearance memory bank and spatial memory bank. In particular, the appearance memory bank utilizes spatial and temporal non-local similarity to propagate segmentation mask to the current frame, and we further treat discriminative correlation filter as spatial memory bank to store the mapping between feature map and spatial map. In particular, we store keys and values of continuous frames in the AMB, and design a memory reader to compute the spatio-temporal attention to previous frames for each pixel in the query image (i.e., the current frame).Thus, albeit the network parameters of the memory module are fixed, we can dynamically update the memory bank to achieve better trade-off between model generalization and flexibility. We further treat DCF as spatial memory bank (SMB) to model the mapping between feature map and spatial map. Moreover, the SMB helps to filter out the dirty samples in AMB while AMB provides SMB with more accurate target geometrical center. This mutual promotion on dual memory banks greatly boost the tracking performance. We also adopt box-to-segmentation training and testing strategy to mitigate inaccurate representation of bounding box initialization during tracking.

Installation

Clone the GIT repository.

git clone https://github.com/phiphiphi31/DMB .

Install dependencies

Run the installation script to install all the dependencies. You need to provide the conda install path (e.g. ~/anaconda3) and the name for the created conda environment (here pytracking).

bash install.sh conda_install_path pytracking

The tracker was tested on the Ubuntu 16.04 machine with 4 NVidia GTX Titan XP graphics card and cudatoolkit version 9.

Test the tracker

1.) Specify the path to the DMB by setting the params.pth_path in the pytracking/parameters/DMB/DMB_default_params.py.
2.) Specify the path to the VOT 2018 dataset by setting the self.data_root in the votTester/vot.py.
3.) Activate the conda environment

conda activate pytracking

4.) Run the run_vot_test.py to run DMB using VOT18 sequences.

python run_vot_test.py

Training the network

The DMB is pre-trained for segmentation task only on the YouTube VOS dataset. Thanks to the training dataset provided from D3S.

D3S - A Discriminative Single Shot Segmentation Tracker.

Please refer to https://github.com/alanlukezic/d3s.git for details to prepare training dataset. Please modify the dataset path in libs/dataset/data.py and libs/train_data/vos.py . Stage-1 pretrained model and training setting file will be available soon.

python train_stage2.py

Pytracking

We use a part of the python framework pytracking based on PyTorch. We would like to thank the authors Martin Danelljan and Goutam Bhat for such a amazing framework. We also thanks to the author Alan Lukežič for his great work D3S!

Contact

• Fei Xie (email: [email protected])