Awesome
CTTrack
The official implementation of the AAAI 2023 Oral paper <u> [Compact Transformer Tracker with Correlative Masked Modeling] </u>
[Models and Raw results] (Google Driver) [Models and Raw results] (Baidu Driver: LRBW)
News
[ Jan 18, 2023]
- We release Code, models and raw results.
[ Jan 11, 2023]
- Our paper is selected for an <font color='red'> oral </font> presentation.
[ Nov 19, 2022]
- CTTrack is accepted to <font color='red'> AAAI2023 </font>.
Strong performance
| Tracker | UAV123 (P) | LaSOT (NP) | TrackingNet (AUC) | GOT-10k (AO) |
|---|---|---|---|---|
| CTTrack | 93.3 | 79.7 | 84.9 | 72.8 |
| MixFormer* (CVPR2022) | 91.0 | 79.9 | 83.9 | 70.7 |
| CSWinTT* (CVPR2022) | 90.3 | 75.2 | 81.9 | 69.4 |
| UTT* (CVPR2022) | - | - | 79.7 | 67.2 |
| STARK* (ICVV2021) | - | 77.0 | 82.0 | 68.8 |
| TransT* (CVPR2021) | 87.6 | 73.8 | 81.4 | 67.1 |
| TrDiMP* (CVPR2021) | 87.6 | 73.2 | 78.4 | 68.8 |
| STMTrack* (CVPR2021) | - | 69.3 | 80.3 | 64.2 |
| AutoMatch* (ICVV2021) | 83.8 | 67.5 | 76.0 | 65.2 |
| SiamGAT* (CVPR2021) | 84.3 | 63.3 | - | 62.7 |
| KYS* (ECCV2020) | - | 63.3 | 74.0 | 63.6 |
| SiamAttn* (CVPR2020) | 84.5 | 64.8 | 75.2 | - |
| SiamFC++* (AAAI2020) | 80.4 | 62.3 | 75.4 | 59.5 |
| SiamRPN++* (CVPR2019) | 84.0 | 56.9 | 73.3 | 51.7 |
| DiMP* (ICCV2019) | 84.9 | 66.4 | 74.0 | 61.1 |
| ATOM* (CVPR2019) | 82.7 | 57.6 | 70.3 | 55.6 |
Install the environment
conda create -n cttrack python=3.7
conda activate cttrack
pip install -r requirements.txt
Data Preparation
Put the tracking datasets in ./data. It should look like:
${CTTRACK_ROOT}
-- data
-- lasot
|-- airplane
|-- basketball
|-- bear
...
-- got10k
|-- test
|-- train
|-- val
-- trackingnet
|-- TRAIN_0
|-- TRAIN_1
...
|-- TRAIN_11
|-- TEST
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 .
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
Train CTTrack
Train CTTrack-B
python tracking/train.py --script cttrack --config baseline --save_dir . --mode single
python tracking/train.py --script cttrack_online --config baseline --save_dir . --mode single --script_prv cttrak --config_prv baseline
Train CTTrack-L
python tracking/train.py --script cttrack --config baseline_L --save_dir . --mode single
python tracking/train.py --script cttrack_online --config baseline_L --save_dir . --mode single --script_prv cttrak --config_prv baseline
Test and evaluate CTTrack on benchmarks
Test CTTrack-B
- OTB2015
python tracking/test.py cttrack baseline --dataset otb --threads 32
- UAV123
python tracking/test.py cttrack baseline --dataset uav --threads 32
- LaSOT
python tracking/test.py cttrack baseline --dataset lasot --threads 32
- GOT10K-test
python tracking/test.py cttrack baseline --dataset got10k_test --threads 32
- TrackingNet
python tracking/test.py cttrack baseline --dataset trackingnet --threads 32
Test CTTrack-L
- OTB2015
python tracking/test.py cttrack baseline_L --dataset otb --threads 32
- UAV123
python tracking/test.py cttrack baseline_L --dataset uav --threads 32
- LaSOT
python tracking/test.py cttrack baseline_L --dataset lasot --threads 32
- GOT10K-test
python tracking/test.py cttrack baseline_L --dataset got10k_test --threads 32
- TrackingNet
python tracking/test.py cttrack baseline_L --dataset trackingnet --threads 32
Evaluate CTTrack
LaSOT/GOT10k-test/TrackingNet/OTB100/UAV123
python tracking/analysis_results.py {script} {config} {dataset_name}
For example
python tracking/analysis_results.py cttrack baseline trackingnet
VOT2020
Before evaluating "CTTrack" on VOT2020, please install VOT toolkit which is required to evaluate our tracker. To download and install VOT toolkit, you can follow this tutorial. For convenience, you can use our example workspaces of VOT toolkit under external/vot20/ by setting trackers.ini.
cd external/vot20/<workspace_dir>
vot evaluate --workspace . CTTrackPython
# generating analysis results
vot analysis --workspace . CTTrackPython --nocache
Visualize attention maps
Model Zoo and raw results
[Models and Raw results] (Google Driver) [Models and Raw results] (Baidu Driver: RLBW)
Contact
Zikai Song: skyesong@hust.edu.cn
Run Luo: lr_8823@hust.edu.cn
Acknowledgments
- Thanks for PyTracking Library, MixFormer Library and MAE Library, which helps us to quickly implement our ideas.
Citation
If you think this project is helpful, please feel free to leave a star⭐️ and cite our paper:
@InProceedings{Song_2023_AAAI,
author = {Song, Zikai and Luo, Run and Yu, Junqing and Chen, Yi-Ping Phoebe and Yang, Wei},
title = {Compact Transformer Tracker with Correlative Masked Modeling},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
month = {February},
year = {2023}
}