ML Anthology

Countor: Count Without Bells and Whistles

Andres Ospina, Felipe Torres
CVPRW 2020 pp. 2559-2565
doi:10.1109/CVPRW50498.2020.00308 /cvprw/2020/ospina2020cvprw-countor/

Abstract

The effectiveness of an Intelligent transportation system (ITS) relies on the understanding of the vehicles behaviour. Different approaches are proposed to extract the attributes of the vehicles as Re-Identification (ReID) or multi-target single camera tracking (MTSC). The analysis of those attributes leads to the behavioural tasks as multi-target multicamera tracking (MTMC) and Turn-counts (Count vehicles that go through a predefined path). In this work, we propose a novel approach to Turn-counts which uses a MTSC and a proposed path classifier. The proposed method is evaluated on CVPR AI City Challenge 2020. Our algorithm achieves the second place in Turn-counts with a score of 0.9346.

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Cite

Text

Ospina and Torres. "Countor: Count Without Bells and Whistles." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00308

Markdown

[Ospina and Torres. "Countor: Count Without Bells and Whistles." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/ospina2020cvprw-countor/) doi:10.1109/CVPRW50498.2020.00308

BibTeX

@inproceedings{ospina2020cvprw-countor,
  title     = {{Countor: Count Without Bells and Whistles}},
  author    = {Ospina, Andres and Torres, Felipe},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {2559-2565},
  doi       = {10.1109/CVPRW50498.2020.00308},
  url       = {https://mlanthology.org/cvprw/2020/ospina2020cvprw-countor/}
}