Video Detection and Classification of Pedestrian Events at Roundabouts and Crosswalks

A well-established technique for studying pedestrian safety is based on reducing data from video-based in-situ observation. The extraction and cataloging from recorded video of pedestrian crossing events has largely been achieved manually. Although the manual methods are generally reliable, they are extremely time-consuming. As a result, more detailed, encompassing site studies are not practical unless the mining for these events can be automated. The study investigated such a tool based on utilizing a novel image processing algorithm recently developed for the extraction of human activities in complex scenes. No human intervention other than defining regions of interest for approaching vehicles and the pedestrian crossing areas was required. The output quantified general event indicators—such as pedestrian wait time, and crossing time and vehicle-pedestrian yield behaviors. Such data can then be used to guide more detailed analyses of the events to study potential vehicle-pedestrian conflicts and their causal effects. The evaluation was done using an extensive set of multi-camera video recordings collected at roundabouts. The tool can be used to support other pedestrian safety research where extracting potential pedestrian-vehicle conflicts from video are required, for example at crosswalks at urban signalized and uncontrolled intersections.

  • Record URL:
  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    University of Minnesota, Twin Cities

    Department of Computer Science, 200 Union Street, SE
    Minneapolis, MN  United States  55455

    Intelligent Transportation Systems Institute

    200 Transportation and Safety Building
    511 Washington Avenue, S.W.
    Minneapolis, MN  United States  55455

    Research and Innovative Technology Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Authors:
    • Morris, Ted
    • Li, Xinyan
    • Morellas, Vassilios
    • Papanikolopoulos, Nikos
  • Publication Date: 2013-8

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Photos; References; Tables;
  • Pagination: 37p

Subject/Index Terms

Filing Info

  • Accession Number: 01493292
  • Record Type: Publication
  • Report/Paper Numbers: CTS 13-27
  • Contract Numbers: CTS Project #2012049
  • Files: UTC, TRIS, RITA, ATRI, USDOT
  • Created Date: Sep 16 2013 2:55PM