azong-hqu/mmtrack

The official implementation for the paper [Towards Unified Token Learning for Vision-Language Tracking].

Towards Unified Token Learning for Vision-Language Tracking (MMTrack)

The official implementation for the TCSVT 2023 paper [Towards Unified Token Learning for Vision-Language Tracking].

[Models] [Raw Results]

☀️ Highlights

Performance

Tracker	TNL2K (AUC)	LaSOT (AUC)	LaSOT-ext (AUC)	OTB99-Lang (AUC)
VLT_{TT}	54.7	67.3	48.4	74.0
JointNLT	56.9	60.4	-	65.3
MMTrack	58.6	70.0	49.4	70.5

Install the environment

conda create -n mmtrack python=3.8
conda activate mmtrack
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

1. Download the preprocessed json file of reforco dataset. If the former link fails, you can download it here.

2. Download the refcoco-train2014 dataset from Joseph Redmon's mscoco mirror.

3. Download the OTB_Lang dataset from Link

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

${PROJECT_ROOT}
 -- data
     -- lasot
         |-- airplane
         |-- basketball
         |-- bear
         ...
     -- tnl2k
         |-- test
         |-- train
     -- refcoco
         |-- images
         |-- refcoco
         |-- refcoco+
         |-- refcocog
     -- otb_lang
         |-- OTB_query_test
         |-- OTB_query_train
         |-- OTB_videos

Training

Dowmload the pretrained OSTrack and Roberta-base, and put it under $PROJECT_ROOT$/pretrained_networks.

python tracking/train.py \
--script mmtrack --config baseline --save_dir ./output \
--mode multiple --nproc_per_node 2 --use_wandb 0

Replace --config with the desired model config under experiments/mmtrack. If you want to use wandb to record detailed training logs, you can set --use_wandb 1.

Evaluation

Download the model weights from Google Drive

Put the downloaded weights on $PROJECT_ROOT$/output/checkpoints/train/mmtrack/baseline

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

Some testing examples:

• LaSOT_lang or other off-line evaluated benchmarks (modify --dataset correspondingly)

python tracking/test.py --tracker_name mmtrack --tracker_param baseline --dataset_name lasot_lang --threads 8 --num_gpus 2

python tracking/analysis_results.py # need to modify tracker configs and names

• lasot_extension_subset_lang

python tracking/test.py --tracker_name mmtrack --tracker_param baseline --dataset_name lasot_extension_subset_lang --threads 8 --num_gpus 2

• TNL2k_Lang

python tracking/test.py --tracker_name mmtrack --tracker_param baseline --dataset_name tnl2k_lang --threads 8 --num_gpus 2

• OTB_Lang

python tracking/test.py --tracker_name mmtrack --tracker_param baseline --dataset_name otb_lang --threads 8 --num_gpus 2

Acknowledgments

• Thanks for the OSTrack, Stable-Pix2Seq and SeqTR library, which helps us to quickly implement our ideas.

Citation

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

@ARTICLE{Zheng2023mmtrack,
  author={Zheng, Yaozong and Zhong, Bineng and Liang, Qihua and Li, Guorong and Ji, Rongrong and Li, Xianxian},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Towards Unified Token Learning for Vision-Language Tracking}, 
  year={2023},
}