https://github.com/GXNU-ZhongLab/AQATrack

CVPR24
https://github.com/GXNU-ZhongLab/AQATrack

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CVPR24

• Host: GitHub
• URL: https://github.com/GXNU-ZhongLab/AQATrack
• Owner: GXNU-ZhongLab
• Created: 2024-03-06T14:32:39.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-06-14T09:54:22.000Z (3 months ago)
• Last Synced: 2024-06-23T15:33:10.456Z (3 months ago)
• Language: Python
• Size: 862 KB
• Stars: 23
• Watchers: 1
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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• Awesome-Visual-Object-Tracking - [code

README

        
# AQATrack

The official implementation for the **CVPR'2024** paper [_Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers_](https://arxiv.org/abs/2403.10574) 

Models:[[Models]](https://drive.google.com/drive/folders/1h0aaZ5rzaYc_0Crd4lZs-ouvFCgWdbyT)

Raw Results:[[Raw Results]](https://drive.google.com/drive/folders/1lx-ge-N3vRZAPLwkyWK5creiQyHktWEz)

Training logs:[[Training logs]](https://drive.google.com/drive/folders/1SB4cry17xNGikJdNmFmoPHP119Yv-1rp)

## :sunny: Structure of AQATrack 

![structure](https://github.com/JinxiaXie/AQATrack/blob/main/assets/arch.png)

## :sunny: Highlights

### :star2: New Autoregressive Query-based Tracking Framework

AQATrack is a simple, high-performance **autoregressive query-based spatio-temporal tracker** for adaptive learning the instantaneous target appearance changes in a sliding window

fashion. Without any additional upadate strategy, AQATrack achieves SOTA performance on multiple benchmarks.

| Tracker     | LaSOT (AUC)|LaSOText (AUC)|UAV123 (AUC)|TrackingNet (AUC)|TNL2K(AUC)|GOT-10K (AO)

|:-----------:|:----------:|:-----------------:|:----------:|:---------------:|:--------:|:----------:

| AQATrack-256| 71.4       | 51.2              | 70.7       | 83.8            | 57.8     | 73.8         

| AQATrack-384| 72.7       | 52.7              | 71.2       | 84.8            | 59.3     | 76.0         

## Install the environment

Use the Anaconda

```

conda create -n aqatrack python=3.8

conda activate aqatrack

bash install.sh

```

## Set project paths

Run the following command to set paths for this project

```

python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir ./output

```

After running this command, you can also modify paths by editing these two files

```

lib/train/admin/local.py  # paths about training

lib/test/evaluation/local.py  # paths about testing

```

## Data Preparation

Put the tracking datasets in ./data. It should look like:

   ```

   ${PROJECT_ROOT}

    -- data

        -- lasot

            |-- airplane

            |-- basketball

            |-- bear

            ...

        -- got10k

            |-- test

            |-- train

            |-- val

        -- coco

            |-- annotations

            |-- images

        -- trackingnet

            |-- TRAIN_0

            |-- TRAIN_1

            ...

            |-- TRAIN_11

            |-- TEST

   ```

## Training

Download pre-trained [HiViT-Base weights](https://drive.google.com/file/d/1VZQz4buhlepZ5akTcEvrA3a_nxsQZ8eQ/view?usp=share_link) and put it under `$PROJECT_ROOT$/pretrained_models` (see [HiViT](https://github.com/zhangxiaosong18/hivit) for more details).

```

bash train.sh

```

## Test

```

python test_epoch.py

```

## Evaluation 

```

python tracking/analysis_results.py

```

## Test FLOPs, and Speed

*Note:* The speeds reported in our paper were tested on a single RTX2080Ti GPU.

```

# Profiling AQATrack-ep150-full-256

python tracking/profile_model.py --script aqatrack --config AQATrack-ep150-full-256

# Profiling AQATrack-ep150-full-384

python tracking/profile_model.py --script aqatrack --config AQATrack-ep150-full-384

```

## Acknowledgments

* Thanks for the [EVPTrack](https://github.com/GXNU-ZhongLab/EVPTrack) and [PyTracking](https://github.com/visionml/pytracking) library, which helps us to quickly implement our ideas.

## Citation

If our work is useful for your research, please consider cite:

```

@inproceedings{xie2024autoregressive,

  title={Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers},

  author={Xie, Jinxia and Zhong, Bineng and Mo, Zhiyi and Zhang, Shengping and Shi, Liangtao and Song, Shuxiang and Ji, Rongrong},

  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

  pages={19300--19309},

  year={2024}

}

```