DUTrack

The official implementation for the CVPR 2025 paper Dynamic Updates for Language Adaptation in Visual-Language Tracking.

[Models][Raw Results][PreTrain]

Highlights

:star2: Dynamic Updates Mutil-Modal Reference for Vision-Language Tracking Framework

DUTrack is a simple yet efficient vision-language tracker with the capability to dynamically update multi-modal references.It achieves state-of-the-art performance on five vision-language tracking benchmarks while maintaining competitive performance on two pure visual tracking benchmarks.

Tracker	LaSOT (AUC)	TNL2K (AUC)	OTB-Lang(AUC)	LaSOT-ext(AUC)	MGIT(SR_IOU)	GOT-10K(AO)
DUTrack-384	74.1	65.6	71.3	52.5	71.0	77.8
DUTrack-256	73.0	64.9	70.9	50.5	70.0	76.7

Install the environment

Conda env: Use the Anaconda (CUDA 11.2)

conda create -n DUTrack python=3.8
conda activate DUTrack
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

Finally, you need to set the model paths for BLIP and BERT in the configuration file.

cfg.MODEL.BERT_DIR: path for [BERT](https://huggingface.co/google-bert/bert-base-uncased) 
cfg.MODEL.BLIP_DIR: path for [BLIP](https://huggingface.co/Salesforce/blip-image-captioning-base) 

Data Preparation

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

${PROJECT_ROOT}
 -- data
     -- lasot
         |-- airplane
         |-- basketball
         |-- bear
         ...
         |-- lasot_train_concise
     -- got10k
         |-- test
         |-- train
             |-- got-10k_train_concise
             ...
         |-- val
     -- TNL2K
         |-- train
         |-- test
         |-- tnl2k_train_concise

Notice ：The files lasot_train_concise, got-10k_train_concise, and tnl2k_train_concise come from DTVLT and contain partial language descriptions from the three datasets.

Training

Download pre-trained and put it under $PROJECT_ROOT$/pretrained_models

python tracking/train.py --script dutrack --config dutrack_256_full --save_dir ./output --mode multiple --nproc_per_node 2 --use_wandb 0

Replace --config with the desired model config under experiments/dutrack.

Evaluation

Download the model weights from Models

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

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 (modify --dataset correspondingly)

python tracking/test.py dutrack dutrack_256_full --dataset lasot --threads 16 --num_gpus 2
python tracking/analysis_results.py # need to modify tracker configs and names

• GOT10K-test

python tracking/test.py dutrack dutrack_256_full --dataset got10k_test --threads 16 --num_gpus 2
python lib/test/utils/transform_got10k.py --tracker_name dutrack --cfg_name dutrack_256_full

• MGIT

python tracking/test.py dutrack dutrack_256_full --dataset --dataset mgit --threads 16 --num_gpus 2

Test FLOPs, and Speed

Note: The speeds reported in our paper were tested on a single V100 GPU.

# Profiling DUTrack-256
python tracking/profile_model.py --script dutrack --config dutrack_256_full
# Profiling DUTrack-384
python tracking/profile_model.py --script dutrack --config dutrack_384_full

Debug

Simply set --debug 1 during inference for debug, e.g.:

python tracking/test.py dutrack dutrack_256_full --dataset --dataset mgit --threads 1 --num_gpus 1 --debug 1

Note ：when setting --debug 1, the tracking results will not be saved as a txt file

Acknowledgments

• Thanks for the ODTrack and PyTracking library, which helps us to quickly implement our ideas.
• We use the implementation of the fast_ITPN from the sunsmarterjie repo.

Citation

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

@inproceedings{Li2025dutrack,
  title={Dynamic Updates for Language Adaptation in Visual-Language Tracking},
  author={Xiaohai Li and and Bineng Zhong and Qihua Liang and Zhiyi Mo and Jian Nong and Shuxiang Song},
  booktitle={CVPR},
  year={2025}
}