ZoomTrack

This is the official implementation for the NeurIPS 2023 spotlight paper ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking

Authors: Yutong KOU, Jin Gao, Bing Li, Gang Wang, Weiming Hu, Yizheng Wang and Liang Li

[Models] [Raw Results] [Training Logs]

Install the environment

Use the Anaconda (CUDA 11.3)

conda env create -f zoom.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

Training

Download pre-trained MAE ViT-Base weights and put it under $PROJECT_ROOT$/pretrained_models (different pretrained models can also be used, see MAE for more details).

python tracking/train.py --script ostrack --config zoom --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 and 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 (modify --dataset correspondingly)

python tracking/test.py ostrack zoom --dataset lasot --threads 8 --num_gpus 4
python tracking/analysis_results.py ostrack zoom lasot

• GOT10K-test

python tracking/test.py ostrack zoom_got --dataset got10k_test --threads 8 --num_gpus 4
python lib/test/utils/transform_got10k.py --tracker_name ostrack --cfg_name zoom_got

• TrackingNet

python tracking/test.py ostrack zoom --dataset trackingnet --threads 8 --num_gpus 4
python lib/test/utils/transform_trackingnet.py --tracker_name ostrack --cfg_name zoom

Visualization or Debug

Visdom is used for visualization.

1. Alive visdom in the server by running visdom:

2. Simply set --debug 5 during inference for visualization, e.g.:

python tracking/test.py ostrack zoom --dataset lasot  --debug 5

3. Open http://localhost:8097 in your browser (remember to change the IP address and port according to the actual situation).

Acknowledgments

• Thanks for the OSTrack and PyTracking library, which helps us to quickly implement our ideas.
• We use the implementation of the ViT from the Timm repo.

Citation

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

@inproceedings{kou2023zoomtrack,
 author = {Kou, Yutong and Gao, Jin and Li, Bing and Wang, Gang and Hu, Weiming and Wang, Yizheng and Li, Liang},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {50959--50977},
 publisher = {Curran Associates, Inc.},
 title = {ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking},
 volume = {36},
 year = {2023}
}