https://github.com/MCG-NJU/TREG

Target Transformed Regression for Accurate Tracking
https://github.com/MCG-NJU/TREG

Last synced: 6 days ago
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Target Transformed Regression for Accurate Tracking

• Host: GitHub
• URL: https://github.com/MCG-NJU/TREG
• Owner: MCG-NJU
• License: gpl-3.0
• Created: 2021-04-01T10:48:32.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-12-05T19:16:51.000Z (almost 3 years ago)
• Last Synced: 2024-08-02T06:13:34.741Z (3 months ago)
• Language: Python
• Size: 1.03 MB
• Stars: 20
• Watchers: 2
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Target Transformed Regression for Accurate Tracking

The official implementation of TREG using PyTorch. 

[[paper]](https://arxiv.org/abs/2104.00403)

[comment]: <> ([[model]](https://drive.google.com/drive/folders/1-TKOF4sKzUUb6C6XfM-rDjrBoEFhovEf?usp=sharing))

![TREG](treg_architecture.png)

## Table of Contents

* [Overview](#overview)

* [Installation](#installation)

* [Training](#training)

* [Test and evaluation](#test-and-evaluation)

* [Citation](#Citation)

## Overview

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

## Installation

Please refer to [INSTALL.md](INSTALL.md) for installation instructions.

We recommend using the [install script](install.sh). 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](https://cis.temple.edu/lasot/), [GOT-10k](http://got-10k.aitestunion.com), [TrackingNet](https://tracking-net.org) 

and [COCO](http://cocodataset.org/#home) 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.

```bibtex

@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}

}

```