https://github.com/hekaijie123/TATrack
Target-Aware Tracking with Long-term Context Attention
https://github.com/hekaijie123/TATrack
Last synced: 3 months ago
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Target-Aware Tracking with Long-term Context Attention
- Host: GitHub
- URL: https://github.com/hekaijie123/TATrack
- Owner: hekaijie123
- License: mit
- Created: 2022-11-21T02:39:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-03-13T04:36:28.000Z (over 2 years ago)
- Last Synced: 2024-11-02T05:32:52.130Z (8 months ago)
- Language: Python
- Size: 917 KB
- Stars: 48
- Watchers: 3
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TATrack
# Target-Aware Tracking with Long-term Context Attention has been accepted by AAAI23.
Law Result and Weights: https://drive.google.com/drive/folders/1PqiciVkwmtD9VCRkHVhZLsLA6cuz5oF1?usp=share_link
## Setup
* Create a new conda environment and activate it.
```Shell
conda create -n TATrack python=3.9 -y
conda activate TATrack
```
* Install `pytorch` and `torchvision`.
```Shell
conda install pytorch torchvision cudatoolkit -c pytorch
```
* Install other required packages.
```Shell
pip install -r requirements.txt
```
## Test
* Prepare the datasets: OTB2015, VOT2018, UAV123, GOT-10k, TrackingNet, LaSOT, COCO*, and something else you want to test. Set the paths as the following:
```Shell
├── TATrack
| ├── ...
| ├── ...
| ├── datasets
| | ├── COCO -> /opt/data/COCO
| | ├── GOT-10k -> /opt/data/GOT-10k
| | ├── LaSOT -> /opt/data/LaSOT/LaSOTBenchmark
| | ├── OTB
| | | └── OTB2015 -> /opt/data/OTB2015
| | ├── TrackingNet -> /opt/data/TrackingNet
| | ├── UAV123 -> /opt/data/UAV123/UAV123
| | ├── VOT
| | | ├── vot2018
| | | | ├── VOT2018 -> /opt/data/VOT2018
| | | | └── VOT2018.json
```
* Notes
> i. Star notation(*): just for training. You can ignore these datasets if you just want to test the tracker.
>
> ii. In this case, we create soft links for every dataset. The real storage location of all datasets is `/opt/data/`. You can change them according to your situation.
>
* Note that all paths we used here are relative, not absolute. See any configuration file in the `experiments` directory for examples and details.
### General command format
```Shell
python main/test.py --config testing_dataset_config_file_path
```
Take GOT-10k as an example:
```Shell
python main/test.py --config experiments/tatrack/test/base/got.yaml
```
## Training
* Prepare the datasets as described in the last subsection.
* Run the shell command.
### training based on the GOT-10k benchmark
```Shell
python main/train.py --config experiments/tatrack/train/base-got.yaml
```
### training with full data
```Shell
python main/train.py --config experiments/tatrack/train/base.yaml
```
# BibTeX
@article{he2023target,
title={Target-Aware Tracking with Long-term Context Attention},
author={He, Kaijie and Zhang, Canlong and Xie, Sheng and Li, Zhixin and Wang, Zhiwen},
journal={arXiv preprint arXiv:2302.13840},
year={2023}
}