ROMTrack

The official implementation of the ICCV 2023 paper Robust Object Modeling for Visual Tracking

[Models and Raw Results] (Google Drive) [Models and Raw Results] (Baidu Netdisk: romt)

📰 News

[September 21, 2023]

We release Models and Raw Results of ROMTrack.
We refine README for more details.

[August 6, 2023]

We release Code of ROMTrack.

[July 14, 2023]

ROMTrack is accepted to ICCV2023.

📆 TODO

Code for ROMTrack
Model Zoo and Raw Results
Refine README

⭐ Highlights

🚀 New Tracking Framework pursing Robustness

ROMTrack employes a robust object modeling design which can keep the inherent information of the target template and enables mutual feature matching between the target and the search region simultaneously.

Robustness Comparison with SOTA methods (bounding box only) on VOT2020.

🚀 Strong Performance and Comparable Speed

Performance on Benchmarks
Speed, MACs, Params (Test on 1080Ti)

📖 Install the environment

Use the Anaconda

conda create -n romtrack python=3.6
conda activate romtrack
bash install_pytorch17.sh

📖 Data Preparation

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

${ROMTrack_ROOT}
 -- data
     -- lasot
         |-- airplane
         |-- basketball
         |-- bear
         ...
     -- lasot_ext
         |-- atv
         |-- badminton
         |-- cosplay
         ...
     -- got10k
         |-- test
         |-- train
         |-- val
     -- coco
         |-- annotations
         |-- train2017
     -- 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 ROMTrack

Training with multiple GPUs using DDP. More details of other training settings can be found at tracking/train_romtrack.sh

bash tracking/train_romtrack.sh

📖 Test and evaluate ROMTrack on benchmarks

LaSOT/LaSOT_ext/GOT10k-test/TrackingNet/OTB100/UAV123/NFS30. More details of test settings can be found at tracking/test_romtrack.sh

bash tracking/test_romtrack.sh

📖 Compute FLOPs/Params and test speed

python tracking/profile_model.py --config="baseline_stage1"

📖 Visualization

We provide attention maps and feature maps for several sequences on LaSOT. Detailed analysis can be found in our paper.

🔖 Acknowledgments

Thanks for STARK, PyTracking and MixFormer Library, which helps us to quickly implement our ideas and test our performances.
Our implementation of the ViT is modified from the Timm repo.

📝 Citation

If our work is useful for your research, please feel free to star:star: and cite our paper:

@article{DBLP:journals/corr/abs-2308-05140,
  author       = {Yidong Cai and
                  Jie Liu and
                  Jie Tang and
                  Gangshan Wu},
  title        = {Robust Object Modeling for Visual Tracking},
  journal      = {CoRR},
  volume       = {abs/2308.05140},
  year         = {2023}
}

liuguoyou / ROMTrack