Publication detail

Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density Based on Computer Vision

ŠPAŇHEL, J. JURÁNEK, R. HEROUT, A. NOVÁK, J. HAVRÁNEK, P.

Original Title

Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density Based on Computer Vision

English Title

Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density Based on Computer Vision

Type

conference paper

Language

en

Original Abstract

The goal of this work was to analyze the behavior of vehicles on third-grade roads with and without horizontal lane markings with small curvature (R  <= 200m). The roads are not frequented by many vehicles, and therefore, a general short-term study would not be able to provide enough data. We used recording devices for long-term (weeks) recording of the traffic and designed a system for analyzing the trajectories of the vehicles employing computer vision. We collected a dataset at 6 distinct locations, containing 1 010 hours of day-time video. In this dataset, we tracked over 12 000 cars and analyzed their trajectories. The results show that the selected approach is functional and provides information that would be hard to mine otherwise. After application of the horizontal markings, the drivers slowed down and shifted slightly towards the outer side of the curvature.

English abstract

The goal of this work was to analyze the behavior of vehicles on third-grade roads with and without horizontal lane markings with small curvature (R  <= 200m). The roads are not frequented by many vehicles, and therefore, a general short-term study would not be able to provide enough data. We used recording devices for long-term (weeks) recording of the traffic and designed a system for analyzing the trajectories of the vehicles employing computer vision. We collected a dataset at 6 distinct locations, containing 1 010 hours of day-time video. In this dataset, we tracked over 12 000 cars and analyzed their trajectories. The results show that the selected approach is functional and provides information that would be hard to mine otherwise. After application of the horizontal markings, the drivers slowed down and shifted slightly towards the outer side of the curvature.

Keywords

Road Safety, Lane Markings, Trajectory Analysis, Computer Vision, Vehicle Tracking

Released

03.07.2019

Publisher

Institute of Electrical and Electronics Engineers

Location

Auckland

ISBN

978-1-5386-7024-8

Book

2019 22th International Conference on Intelligenet Transportation Systems (ITSC)

Edition

NEUVEDEN

Edition number

NEUVEDEN

Pages from

1001

Pages to

1006

Pages count

6

URL

Documents

BibTex


@inproceedings{BUT161457,
  author="Jakub {Špaňhel} and Roman {Juránek} and Adam {Herout}",
  title="Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density Based on Computer Vision",
  annote="The goal of this work was to analyze the behavior of vehicles on third-grade
roads with and without horizontal lane markings with small curvature (R  <=
200m). The roads are not frequented by many vehicles, and therefore, a general
short-term study would not be able to provide enough data. We used recording
devices for long-term (weeks) recording of the traffic and designed a system for
analyzing the trajectories of the vehicles employing computer vision. We
collected a dataset at 6 distinct locations, containing 1 010 hours of day-time
video. In this dataset, we tracked over 12 000 cars and analyzed their
trajectories. The results show that the selected approach is functional and
provides information that would be hard to mine otherwise. After application of
the horizontal markings, the drivers slowed down and shifted slightly towards the
outer side of the curvature.",
  address="Institute of Electrical and Electronics Engineers",
  booktitle="2019 22th International Conference on Intelligenet Transportation Systems (ITSC)",
  chapter="161457",
  doi="10.1109/ITSC.2019.8917374",
  edition="NEUVEDEN",
  howpublished="online",
  institution="Institute of Electrical and Electronics Engineers",
  year="2019",
  month="july",
  pages="1001--1006",
  publisher="Institute of Electrical and Electronics Engineers",
  type="conference paper"
}