ML Anthology

Determining Vehicle Turn Counts at Multiple Intersections by Separated Vehicle Classes Using CNNs

Ján Folenta, Jakub Spanhel, Vojtech Bartl, Adam Herout
CVPRW 2020 pp. 2544-2549
doi:10.1109/CVPRW50498.2020.00306 /cvprw/2020/folenta2020cvprw-determining/

Abstract

In our submission to the NVIDIA AI City Challenge 2020, we address the problem of counting vehicles by their class at multiple intersections. Our solution is based on counting by tracking principle using convolutional neural networks in detection and tracking steps of the proposed method. We have achieved 6th place on the dataset part A of Track 1 with score S1 Total = 0.8829, (mwRMSE = 4.3616, S1 Effectiveness = 0.9094, S1 Efficiency = 0.8212). The proposed solution was placed at sixth place in the overall ranking on dataset part A.

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Text

Folenta et al. "Determining Vehicle Turn Counts at Multiple Intersections by Separated Vehicle Classes Using CNNs." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00306

Markdown

[Folenta et al. "Determining Vehicle Turn Counts at Multiple Intersections by Separated Vehicle Classes Using CNNs." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/folenta2020cvprw-determining/) doi:10.1109/CVPRW50498.2020.00306

BibTeX

@inproceedings{folenta2020cvprw-determining,
  title     = {{Determining Vehicle Turn Counts at Multiple Intersections by Separated Vehicle Classes Using CNNs}},
  author    = {Folenta, Ján and Spanhel, Jakub and Bartl, Vojtech and Herout, Adam},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {2544-2549},
  doi       = {10.1109/CVPRW50498.2020.00306},
  url       = {https://mlanthology.org/cvprw/2020/folenta2020cvprw-determining/}
}