AIC2018 Report: Traffic Surveillance Research

Tingyu Mao, Wei Zhang, Haoyu He, Yanjun Lin, Vinay Kale, Alexander Stein, Zoran Kostic

CVPRW 2018 pp. 85-92

doi:10.1109/CVPRW.2018.00019 /cvprw/2018/mao2018cvprw-aic2018/

Abstract

Traffic surveillance and management technologies are some of the most intriguing aspects of smart city applications. In this paper, we investigate and present the methods for vehicle detections, tracking, speed estimation and anomaly detection for NVIDIA AI City Challenge 2018 (AIC2018). We applied Mask-RCNN and deep-sort for vehicle detection and tracking in track 1, and optical flow based method in track 2. In track 1, we achieve 100% detection rate and 7.97 mile/hour estimation error for speed estimation.

CVPRW Semantic Scholar

Cite

Text

Mao et al. "AIC2018 Report: Traffic Surveillance Research." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00019

Markdown

[Mao et al. "AIC2018 Report: Traffic Surveillance Research." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/mao2018cvprw-aic2018/) doi:10.1109/CVPRW.2018.00019

BibTeX

@inproceedings{mao2018cvprw-aic2018,
  title     = {{AIC2018 Report: Traffic Surveillance Research}},
  author    = {Mao, Tingyu and Zhang, Wei and He, Haoyu and Lin, Yanjun and Kale, Vinay and Stein, Alexander and Kostic, Zoran},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {85-92},
  doi       = {10.1109/CVPRW.2018.00019},
  url       = {https://mlanthology.org/cvprw/2018/mao2018cvprw-aic2018/}
}