Awesome

Target Transformed Regression for Accurate Tracking

The official implementation of TREG using PyTorch.

[paper]

Table of Contents

• Overview
• Installation
• Training
• Test and evaluation
• Citation

Overview

In this repo, we provide pretrained model, training and inference code for TREG.

Installation

Please refer to INSTALL.md for installation instructions. We recommend using the install script. Before running the installation script, make sure you have installed conda with python 3.7 and cuda10.0. And our platform is ubuntu 18.04.

./install.sh YOUR_CONDA_INSTALL_PATH ENVIRONMENT_NAME

Training

We use Lasot, GOT-10k, TrackingNet and COCO to train TREG.

cd bash
./train_treg.sh

Test and evaluation

In the pytracking directory, you can test trackers on a set of datasets and use integrated evaluation APIs to evaluate the tracking results.

1. Run the tracker on a set of datasets

cd bash
./run_treg_on_otb.sh

See scripts under bin for the more supported datasets.

2. Evaluate the tracking results on datasets

cd bash
./eval_treg_on_otb.sh

See scripts under bin for the more scripts to evaluate on other datasets.

For GOT-10k, TrackingNet, you need to evaluate results on official server, we provide tools to pack tracking results into the zipfile of submission format. Also, put the tracking results under results_path/treg, you can use the script to pack trackingnet results:

cd bash
./pack_results_on_tn.sh

The packed zipfile can be found in the path packed_results_path that you set in local.py.

Citation

Please consider citing our paper in your publications if the project helps your research.

@article{treg2021,
  author    = {Yutao Cui and
               Cheng Jiang and
               Limin Wang and
               Gangshan Wu},
  title     = {Target Transformed Regression for Accurate Tracking},
  journal   = {CoRR},
  volume    = {abs/2104.00403},
  year      = {2021}
}