Mobile Vision Transformer-based Visual Object Tracking [BMVC2023] official implementation

News

11-03-2024: C++ implementation of our tracker is available now

10-11-2023: ONNX-Runtime and TensorRT-based inference code is released. Now, our MVT runs at ~70 fps on CPU and ~300 fps on GPU ⚡⚡. Check the page for details.

14-09-2023: The pretrained tracker model is released

13-09-2023: The paper is available on arXiv now

22-08-2023: The MVT tracker training and inference code is released

21-08-2023: The paper is accepted at BMVC2023

Installation

Install the dependency packages using the environment file mvt_pyenv.yml.

Generate the relevant files:

python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir ./output

After running this command, modify the datasets paths by editing these files

lib/train/admin/local.py  # paths about training
lib/test/evaluation/local.py  # paths about testing

Training

• Set the path of training datasets in lib/train/admin/local.py
• Place the pretrained backbone model under the pretrained_models/ folder
• For data preparation, please refer to this
• Uncomment lines 63, 67, and 71 in the base_backbone.py file. Replace these lines with self.z_dict1 = template.tensors.
• Run

python tracking/train.py --script mobilevit_track --config mobilevit_256_128x1_got10k_ep100_cosine_annealing --save_dir ./output --mode single

• The training logs will be saved under output/logs/ folder

Pretrained tracker model

The pretrained tracker model can be found here

Tracker Evaluation

• Update the test dataset paths in lib/test/evaluation/local.py
• Place the pretrained tracker model under output/checkpoints/ folder
• Run

python tracking/test.py --tracker_name mobilevit_track --tracker_param mobilevit_256_128x1_got10k_ep100_cosine_annealing --dataset got10k_test/trackingnet/lasot

• Change the DEVICE variable between cuda and cpu in the --tracker_param file for GPU and CPU-based inference, respectively
• The raw results will be stored under output/test/ folder

Profile tracker model

• To count the model parameters, run

python tracking/profile_model.py

Acknowledgements

• We use the Separable Self-Attention Transformer implementation and the pretrained MobileViT backbone from ml-cvnets. Thank you!
• Our training code is built upon OSTrack and PyTracking

Citation

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

@inproceedings{Gopal_2023_BMVC,
author    = {Goutam Yelluru Gopal and Maria Amer},
title     = {Mobile Vision Transformer-based Visual Object Tracking},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year      = {2023},
url       = {https://papers.bmvc2023.org/0800.pdf}
}