ML Anthology

Predicting the Perceptual Demands of Urban Driving with Video Regression

Luke Palmer, Alina Bialkowski, Gabriel J. Brostow, Jonas Ambeck-Madsen, Nilli Lavie
WACV 2017 pp. 409-417
doi:10.1109/WACV.2017.52 /wacv/2017/palmer2017wacv-predicting/

Abstract

To drive safely requires perceiving vast amounts of rapidly changing visual information. This can exhaust our limited perceptual capacity and lead to cases of 'looking but failing to see', reportedly the third largest contributing factor to road traffic accidents. In the present work we use a 3D convolutional neural network to model the perceptual demand of varied driving situations. To validate the method we introduce a new labelled dataset of approximately 2300 videos of driving in Brussels and California.

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Cite

Text

Palmer et al. "Predicting the Perceptual Demands of Urban Driving with Video Regression." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017. doi:10.1109/WACV.2017.52

Markdown

[Palmer et al. "Predicting the Perceptual Demands of Urban Driving with Video Regression." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017.](https://mlanthology.org/wacv/2017/palmer2017wacv-predicting/) doi:10.1109/WACV.2017.52

BibTeX

@inproceedings{palmer2017wacv-predicting,
  title     = {{Predicting the Perceptual Demands of Urban Driving with Video Regression}},
  author    = {Palmer, Luke and Bialkowski, Alina and Brostow, Gabriel J. and Ambeck-Madsen, Jonas and Lavie, Nilli},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2017},
  pages     = {409-417},
  doi       = {10.1109/WACV.2017.52},
  url       = {https://mlanthology.org/wacv/2017/palmer2017wacv-predicting/}
}