Geometry-Aware Traffic Flow Analysis by Detection and Tracking

Honghui Shi, Zhonghao Wang, Yang Zhang, Xinchao Wang, Thomas S. Huang

CVPRW 2018 pp. 116-120

doi:10.1109/CVPRW.2018.00023 /cvprw/2018/shi2018cvprw-geometryaware/

Abstract

In the second Nvidia AI City Challenge hosted in 2018, the traffic flow analysis challenge proposes an interest task that requires participants to predict the speed of vehicles on road from various traffic camera videos. We propose a simple yet effective method combing both learning based detection and geometric calibration based estimation. We use a learning based method to detect and track vehicles, and use a geometry based camera calibration method to calculate the speed of those vehicles. We achieve a perfect detection rate of target vehicles and a root mean square error (RMSE) of 6.6674 in predicting the vehicle speed, which rank us the third place in the competition.

CVPRW Semantic Scholar

Cite

Text

Shi et al. "Geometry-Aware Traffic Flow Analysis by Detection and Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00023

Markdown

[Shi et al. "Geometry-Aware Traffic Flow Analysis by Detection and Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/shi2018cvprw-geometryaware/) doi:10.1109/CVPRW.2018.00023

BibTeX

@inproceedings{shi2018cvprw-geometryaware,
  title     = {{Geometry-Aware Traffic Flow Analysis by Detection and Tracking}},
  author    = {Shi, Honghui and Wang, Zhonghao and Zhang, Yang and Wang, Xinchao and Huang, Thomas S.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {116-120},
  doi       = {10.1109/CVPRW.2018.00023},
  url       = {https://mlanthology.org/cvprw/2018/shi2018cvprw-geometryaware/}
}