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:
  Actuated Traffic Signal Controllers Adaptive Control Crosswalks Pedestrian Actuated Controllers Pedestrian Detectors Pedestrian Safety
• 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.
  
  
• Format:
  PDF
• Funding:
  Contract No. DTRT12GUTG11
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:decc2cf80c2413b0ea45b560200a83283dc3de8b54e2455f28625a9063e2b9ad3ff100bd5a655b7ae3d91d71b10c0c3f8f91b1c2aa3001d3fac25e2f53ee49ed
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31314/dot_31314_DS1.pdf
• File Type:
  [PDF - 1.63 MB ]