Awesome

E.T.Track - Efficient Visual Tracking with Exemplar Transformers

Official implementation of E.T.Track. E.T.Track utilized our proposed Exemplar Transformer, a transformer module utilizing a single instance level attention layer for realtime visual object tracking. E.T.Track is up to 8x faster than other transformer-based models, and consistently outperforms competing lightweight trackers that can operate in realtime on standard CPUs.

Installation

Install dependencies

Install the python environment using the environment file ettrack_env.yml.

Generate the relevant files:

python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()"
python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()"

• Modify local.py. Modify the path files for the evaluation in pytracking/evaluation/local.py

Evaluation

We evaluate our models using PyTracking. The sequences comparing E.T.Track and LT-Mobile in the ''Video Visualizations'' section can be found here.

• Add the correct dataset in pytracking/experiments/myexperiments.py (default: OTB-100)
• Run python3 -m pytracking.run_experiment myexperiments et_tracker --threads 0

Citation

If you use this code, please consider citing the following paper:

@inproceedings{blatter2023efficient,
  title={Efficient visual tracking with exemplar transformers},
  author={Blatter, Philippe and Kanakis, Menelaos and Danelljan, Martin and Van Gool, Luc},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1571--1581},
  year={2023}
}