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Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection

Editted by Chengyang Li, Zhejiang University.

Detection performance

Note: Since the original annotations of the test set contain many problematic bounding boxes, we use the improved testing annotations provided by Liu et al. to enable a reliable comparison.

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Detection results

Citing our paper

If you find our work useful in your research, please consider citing:

@article{li2019illumination,
  title={Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection},
  author={Li, Chengyang and Song, Dan and Tong, Ruofeng and Tang, Min},
  journal={Pattern Recognition},
  volume={85C},
  pages={161-171},
  year={2019},
  publisher={Elsevier}
}

iaf-rcnn's People

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iaf-rcnn's Issues

Complete code and pre-trained models

Hi Sir,
Thank you for your great contribution in this domain. I am interested in your research work , is it possible to provide complete code and pre-trained models?

I'll be very thankful to you.

Best regards.
Muhammad Asad Javed
School of Computer Science and Technology,
Beijing Institute of Technology,
Beijing,China.

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