matteo-dunnhofer / cocolot

Python implementation of the CoCoLoT tracker (aka mlpLT), winner of the Visual Object Tracking VOT2021 Long-term Challenge.

CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking

🏆 Winner of the Visual Object Tracking VOT2021 Long-term Challenge (aka mlpLT)

Papers: ICPR 2022 (Oral), IMAVIS (Extended version)

Matteo Dunnhofer, Kristian Simonato, Christian Micheloni

Machine Learning and Perception Lab
Department of Mathematics, Computer Science and Physics
University of Udine
Udine, Italy

Hardware and OS specifications

CPU Intel Xeon E5-2690 v4 @ 2.60GHz GPU NVIDIA TITAN V 320 GB of RAM OS: Ubuntu 20.04

VOT-LT test instructions

To run the VOT Challenge Long-term experiments please follow these instructions:

• Clone the repository git clone https://github.com/matteo-dunnhofer/CoCoLoT

• Download the pre-trained weights files STARKST_ep0050.pth.tar, super_dimp.pth.tar, metric_model.pt from here and put them in the folder CoCoLoT/ckpt/

• Move to the submission source folder cd CoCoLoT

• Create the Anaconda environment conda env create -f environment.yml

• Activate the environment conda activate CoCoLoT

• Install ninja-build sudo apt-get install ninja-build

• Edit the variable base_path in the file vot_path.py by providing the full-path to the location where the submission folder is stored, and do the same in the file trackers.ini by substituting the paths [full-path-to-CoCoLoT] in line 9 and 13

• Run python compile_pytracking.py

• Run the analysis by vot evaluate CoCoLoT

• Run the evaluation by vot analysis CoCoLoT

If you fail to run our tracker please write to [email protected]

An improved version of CoCoLoT (submitted to the VOT2022 Challenge) exploiting Stark and KeepTrack is downloadable here.

References

If you find this code useful please cite:

@inproceedings{Dunnhofer2022icpr,
    author={Dunnhofer, Matteo and Micheloni, Christian},
    booktitle={2022 26th International Conference on Pattern Recognition (ICPR)}, 
    title={CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking}, 
    year={2022},
    pages={5132-5139},
    doi={10.1109/ICPR56361.2022.9956082}
}

@article{Dunnhofer2022imavis,
    title = {Combining complementary trackers for enhanced long-term visual object tracking},
    journal = {Image and Vision Computing},
    volume = {122},
    year = {2022},
    doi = {https://doi.org/10.1016/j.imavis.2022.104448}
}

@InProceedings{Kristan_2021_ICCV,
    author    = {Kristan, Matej and Matas, Ji\v{r}{\'\i} and Leonardis, Ale\v{s} and Felsberg, Michael and Pflugfelder, Roman and K\"am\"ar\"ainen, Joni-Kristian and Chang, Hyung Jin and Danelljan, Martin and Cehovin, Luka and Luke\v{z}i\v{c}, Alan and Drbohlav, Ondrej and K\"apyl\"a, Jani and H\"ager, Gustav and Yan, Song and Yang, Jinyu and Zhang, Zhongqun and Fern\'andez, Gustavo},
    title     = {The Ninth Visual Object Tracking VOT2021 Challenge Results},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    month     = {October},
    year      = {2021},
    pages     = {2711-2738}
}

The code presented here is built up on the following repositories:

• pytracking
• Stark
• LTMU

Copyright © Machine Learning and Perception Lab - University of Udine - 2021 - 2022