The code and weights are still being prepared and organized, please stay tuned.

LoReTrack

This project provides the code and results for LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking, IROS 2025 (Oral)

Authors: Shaohua Dong, Yunhe Feng, James Liang, Qing Yang, Yuewei Lin, Heng Fan

PaperLink: https://arxiv.org/pdf/2405.17660

Highlights

LoReTrack introduces a resolution-scalable Transformer tracker that achieves efficient and accurate low-resolution tracking by distilling global and target-aware knowledge from a frozen high-resolution counterpart through two simple yet effective distillation modules, which can also be easily applied to other trackers.

Results and Weights

Tracker	LaSOT (AUC)	LaSOText (AUC)	GOT-10k (AO)	TrackingNet (AUC)	UAV123 (AUC)	Device	FPS
LoReTrack-256	70.3 weight raw results	51.3 weight raw results	73.5 weight raw results	82.9 weight raw results	70.6 weight raw results	GPU	130
LoReTrack-192	68.6 weight raw results	50.0 weight raw results	71.5 weight raw results	80.9 weight raw results	69.9 weight raw results	GPU	186
LoReTrack-128	64.9 weight raw results	46.4 weight raw results	64.3 weight raw results	77.7 weight raw results	69.0 weight raw results	CPU	25
LoReTrack-96	61.0 weight raw results	45.1 weight raw results	58.9 weight raw results	74.0 weight raw results	67.1 weight raw results	CPU	31

The inference was performed using a single NVIDIA RTX A5500 GPU and an Intel Core i9-11900K CPU.

Install the environment (Follow OSTrack)

Option1: Use the Anaconda (CUDA 10.2)

conda create -n ostrack python=3.8
conda activate ostrack
bash install.sh

Option2: Use the Anaconda (CUDA 11.3)

conda env create -f ostrack_cuda113_env.yaml

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 this:

${PROJECT_ROOT}
 -- data
     -- lasot
         |-- airplane
         |-- basketball
         |-- bear
         ...
     -- got10k
         |-- test
         |-- train
         |-- val
     -- coco
         |-- annotations
         |-- images
     -- trackingnet
         |-- TRAIN_0
         |-- TRAIN_1
         ...
         |-- TRAIN_11
         |-- TEST

For the LaSOText and UAV123 datasets, only testing was performed.

Training

Download OSTrack weights and put it under $PROJECT_ROOT$/pretrained_models.

python tracking/train.py --script ostrack --config vitb_256_mae_32x4_ep300 --save_dir ./output --mode multiple --nproc_per_node 4 --use_wandb 1

Replace --config with the desired model config under experiments/ostrack. We use wandb to record detailed training logs, in case you don't want to use wandb, set --use_wandb 0.

Evaluation

Download the model weights from Google Drive, as linked in the table above.

Put the downloaded weights on $PROJECT_ROOT$/output/checkpoints/train/ostrack

Change the corresponding values of lib/test/evaluation/local.py to the actual benchmark saving paths

Some testing examples:

• LaSOT or other off-line evaluated benchmarks (LaSOText, UAV123) (modify --dataset correspondingly)

python tracking/test.py ostrack vitb_384_mae_32x4_ep300 --dataset lasot --threads 16 --num_gpus 4
python tracking/analysis_results.py # need to modify tracker configs and names

• GOT10K-test

python tracking/test.py ostrack vitb_384_mae_32x4_got10k_ep100 --dataset got10k_test --threads 16 --num_gpus 4
python lib/test/utils/transform_got10k.py --tracker_name ostrack --cfg_name vitb_384_mae_32x4_got10k_ep100

• TrackingNet

python tracking/test.py ostrack vitb_384_mae_32x4_ep300 --dataset trackingnet --threads 16 --num_gpus 4
python lib/test/utils/transform_trackingnet.py --tracker_name ostrack --cfg_name vitb_384_mae_32x4_ep300

Test FLOPs, and Speed

# Profiling vitb_256_mae_32x4_ep300
python tracking/profile_model.py --script ostrack --config vitb_256_mae_32x4_ep300

Acknowledgments

• Thanks for the OSTrack and PyTracking library, which helps us to quickly implement our ideas.

Citation

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

@article{dong2024loretrack,
  title={Loretrack: efficient and accurate low-resolution transformer tracking},
  author={Dong, Shaohua and Feng, Yunhe and Yang, Qing and Lin, Yuewei and Fan, Heng},
  journal={arXiv preprint arXiv:2405.17660},
  year={2024}
}