ML Anthology

SqueezeMap: Fast Pedestrian Detection on a Low-Power Automotive Processor Using Efficient Convolutional Neural Networks

Rytis Verbickas, Robert Laganière, Daniel Laroche, Changyun Zhu, Xiaoyin Xu, Ali Ors
CVPRW 2017 pp. 463-471
doi:10.1109/CVPRW.2017.62 /cvprw/2017/verbickas2017cvprw-squeezemap/

Abstract

Pedestrian detection for autonomous driving is a challenging task that requires careful trade-off between accuracy, storage, computation and energy requirements. In our work, we extend the recent SqueezeNet architecture to pedestrian detection. We show how this network can be modified to obtain detection performance on the Caltech USA pedestrian dataset that is comparable in overall log-average miss rate to other competing models while easily running at 30FPS on an automotive processor with a model size of 3.24MB and within a power envelope of 2W. The extension relies on the observation that precise knowledge of bounding box corners is not necessary to know the location of pedestrians and their approximate size. Rather, a coarse grid based localization is proposed here and acts as a kind of heatmap of pedestrian locations, relying on only one forward pass through the network. The number of new free parameters introduced is small relative to the original SqueezeNet model.

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Cite

Text

Verbickas et al. "SqueezeMap: Fast Pedestrian Detection on a Low-Power Automotive Processor Using Efficient Convolutional Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.62

Markdown

[Verbickas et al. "SqueezeMap: Fast Pedestrian Detection on a Low-Power Automotive Processor Using Efficient Convolutional Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/verbickas2017cvprw-squeezemap/) doi:10.1109/CVPRW.2017.62

BibTeX

@inproceedings{verbickas2017cvprw-squeezemap,
  title     = {{SqueezeMap: Fast Pedestrian Detection on a Low-Power Automotive Processor Using Efficient Convolutional Neural Networks}},
  author    = {Verbickas, Rytis and Laganière, Robert and Laroche, Daniel and Zhu, Changyun and Xu, Xiaoyin and Ors, Ali},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2017},
  pages     = {463-471},
  doi       = {10.1109/CVPRW.2017.62},
  url       = {https://mlanthology.org/cvprw/2017/verbickas2017cvprw-squeezemap/}
}