Pedestrian-Friendly Traffic Signal Control

This project continues research aimed at real-time detection and use of pedestrian traffic flow information to enhance adaptive traffic signal control in urban areas where pedestrian traffic is substantial and must be given appropriate attention and priority. The research team's recent work with Surtrac [12], a real-time adaptive signal control system for urban grid networks, has resulted in an extended intersection scheduling procedure that integrates sensed pedestrians and vehicles into aggregate multi-modal traffic flows and allocates green time on this integrated basis [17]. In this project the research team considers the companion problem of providing the pedestrian sensing capability necessary for effective use of this extended intersection scheduling procedure. Although some commercial pedestrian detection and counting capabilities do exist, they typically require the purchase and installation of additional higher resolution video camera technology, which can double the cost of detection per intersection. The research team's interest is in a solution that does not significantly increase infrastructure cost. The hypothesis investigated in this work is that lower resolution vehicle detection camera technology can be used to provide a relaxed form of pedestrian count data that is sufficient for incorporating pedestrian flow information into real-time intersection scheduling. Specifically, the research team studies the possibility of extracting an approximate but usable measure of pedestrian “density” from the video stream of a commercial traffic camera. The research team's target functionality is the ability to qualitatively discriminate between “no”, “few” or “many” waiting pedestrians. Contemporary traffic camera technologies provide resolution as low as 320 × 240 gray scale images (see Figure 1), together with the ability to specify and monitor a set of occupancy zones within the image. Pedestrian detection and counting is not a hard task for humans, but it is challenging for computers. The challenges include diverse shapes and occlusion among pedestrians, a dynamic background, and low video quality. First, pedestrians can have various appearances because of clothing, accessories, assistive devices, and change of pose while walking. This high intra-class variation, as well as occlusion, makes pedestrian detection a hard problem. An alternative to classification based pedestrian detection is to find foreground pixels in each frame of the video, and analyze those pixels to estimate the number of pedestrians. However, since the system is deployed in an outdoor environment, shadows caused by moving objects or sudden change in illumination can create noise that complicates foreground detection. Moreover, to get a broader view of the intersection, the camera is installed at a certain height. Thus, pedestrians are small in the images and have fewer details for computer vision processing. These challenges have made pedestrian detection and counting a topic of interest in computer vision research for many years, and several classes of techniques have been developed. At the same time, this field of research has invariably assumed the availability and use of state of the art detection hardware. The concept of “pedestrian detection on a budget”, as typified by the research team's goal of exploiting existing vehicle detection camera technology to do double duty and detect waiting pedestrian volumes, has received little attention.


    • English


    • Status: Completed
    • Contract Numbers:


    • Sponsor Organizations:

      Carnegie Mellon University

      Pittsburgh, PA  United States 

      Technologies for Safe and Efficient Transportation University Transportation Center

      Carnegie Mellon University
      Pittsburgh, PA  United States  15213

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Project Managers:

      Ehrlichman, Courtney

    • Start Date: 20140131
    • Expected Completion Date: 0
    • Actual Completion Date: 20141231
    • Source Data: RiP Project 36282

    Subject/Index Terms

    Filing Info

    • Accession Number: 01518463
    • Record Type: Research project
    • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
    • Contract Numbers: DTRT12GUTG11
    • Files: UTC, RiP
    • Created Date: Mar 20 2014 1:00AM