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[TPAMI 2018] Predicting the Driver’s Focus of Attention: the DR(eye)VE Project. A deep neural network learnt to reproduce the human driver focus of attention (FoA) in a variety of real-world driving scenarios.

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DR(eye)VE Project: code repository

A deep neural network trained to reproduce the human driver focus of attention.

Results (video)

video_results

How-To

This repository was used throughout the whole work presented in the paper so it contains quite a large amount of code. Nonetheless, it should be quite easy to navigate into. In particular:

  • docs: project supplementary website, holding some additional information concerning the paper.
  • dreyeve-tobii: cpp code to acquire gaze over dreyeve sequences with Tobii EyeX.
  • semseg: python project to calculate semantic segmentation over all frames of a dreyeve sequence
  • experiments: python project that holds stuff for experimental section
  • matlab: some matlab code to compute optical flow, blends or to create the new fixation groundtruth.

The experiments section is the one that probably interest the reader, in that is the one that contains the code used for developing and training both our model and baselines and competitors. More detailed documentation is available there.

All python code has been developed and tested with Keras 1 and using Theano as backend.

Pre-trained weights:

Pre-trained weights of the multi-branch model can be downloaded from this link.


The code accompanies the following paper:

  @article{palazzi2018predicting,
  title={Predicting the Driver's Focus of Attention: the DR (eye) VE Project},
  author={Palazzi, Andrea and Abati, Davide and Solera, Francesco and Cucchiara, Rita},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={41},
  number={7},
  pages={1720--1733},
  year={2018},
  publisher={IEEE}
}

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[TPAMI 2018] Predicting the Driver’s Focus of Attention: the DR(eye)VE Project. A deep neural network learnt to reproduce the human driver focus of attention (FoA) in a variety of real-world driving scenarios.

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