Pedestrian Friendly Traffic Signal Control.

Details

• Creators:
  Smith, Stephen F. ; Barlow, Gregory J. ; Hu, Hsu-Chieh ; Hua, Ju-Hsuan
• Corporate Creators:
  Carnegie-Mellon University ; University of Pennsylvania
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Adaptive control
  • Pedestrian actuated controllers
  • Pedestrian safety
  • Pedestrian detectors
  • Actuated traffic signal controllers
  • Crosswalks
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  University Transportation Centers Program (U.S.)
• Abstract:
  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. Our 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 we ; consider 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. ; Our 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, we study the possibility of extracting an ; approximate but usable measure of pedestrian “density” from the video stream of a ; commercial traffic camera. Our 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.
  More +
• Format:
  PDF
• Funding:
  Contract No. DTRT12GUTG11
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31314/dot_31314_DS1.pdf
• File Type:
  [PDF - 1.63 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:decc2cf80c2413b0ea45b560200a83283dc3de8b54e2455f28625a9063e2b9ad3ff100bd5a655b7ae3d91d71b10c0c3f8f91b1c2aa3001d3fac25e2f53ee49ed