zhanglichao / generatedTIR_tracking

Synthetic data generation for end-to-end TIR tracking (TIP2018)

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Synthetic data generation for end-to-end TIR tracking [paper]

Citation

Please cite our paper if you are inspired by this idea.

@article{zhang2018synthetic,
  title={Synthetic data generation for end-to-end thermal infrared tracking},
  author={Zhang, Lichao and Gonzalez-Garcia, Abel and van de Weijer, Joost and Danelljan, Martin and Khan, Fahad Shahbaz},
  journal={IEEE Transactions on Image Processing},
  volume={28},
  number={4},
  pages={1837--1850},
  year={2018},
  publisher={IEEE}
}

Instructions

This project is to transfer RGB tracking videos to TIR tracking videos in order to complement the TIR data for training. We give two kinds of models corresponding for two-stage of our porject (The transferring stage and the fine-tuning stage).

Analysis for RGB and TIR


Results for the two image translation methods considered: pix2pix and CycleGAN. On the test set of KAIST[1].


The left is the Average activation of filters from the first layer of pre-trained AlexNet. The right is the Histogram of the gradient magnitude for real and synthetic TIR data.

Models

Results

References

[1] Hwang, Soonmin and Park, Jaesik and Kim, Namil and Choi, Yukyung and So Kweon, In.
Multispectral pedestrian detection: Benchmark dataset and baseline.
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

Contact

For further inquries please contact with me: lichao@cvc.uab.es. Or submit a bug report on the Github site of the project.

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Synthetic data generation for end-to-end TIR tracking (TIP2018)


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