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.
| E.T.Track | The standard attention vs our Exemplar Attention on the right |
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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
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
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}
}