Leveraging traffic and surveillance video cameras for urban traffic.

Details

• Creators:
  Eriksson, Jakob
• Corporate Creators:
  University of Illinois at Chicago. Dept. of Computer Science
• Corporate Contributors:
  Illinois. Dept. of Transportation. Bureau of Materials and Physical Research ; United States. Federal Highway Administration
• Subject/TRT Terms:
  • Video cameras
  • Traffic data
  • Data collection
  • Real time information
  • Pedestrians
  • Cyclists
  • Urban areas
  • Traffic surveillance
  • Vehicle trajectories
  • Detection and identification devices
• Publication/ Report Number:
  FHWA-ICT-14-022 ; ICT-14-024 ; UILU-ENG-2014-2026
• Resource Type:
  Tech Report
• Geographical Coverage:
  Illinois
• Corporate Publisher:
  Illinois Center for Transportation
• Abstract:
  The objective of this project was to investigate the use of existing video resources, such as traffic ; cameras, police cameras, red light cameras, and security cameras for the long-term, real-time ; collection of traffic statistics. An additional objective was to gather similar statistics for pedestrians and ; bicyclists. Throughout the course of the project, we investigated several methods for tracking vehicles ; under challenging conditions. The initial plan called for tracking based on optical flow. However, it was ; found that current optical flow–estimating algorithms are not well suited to low-quality video—hence, ; developing optical flow methods for low-quality video has been one aspect of this project. The method ; eventually used combines basic optical flow tracking with a learning detector for each tracked object— ; that is, the object is tracked both by its apparent movement and by its appearance should it temporarily ; disappear from or be obscured in the frame. We have produced a prototype software that allows the ; user to specify the vehicle trajectories of interest by drawing their shapes superimposed on a video ; frame. The software then tracks each vehicle as it travels through the frame, matches the vehicle’s ; movements to the most closely matching trajectory, and increases the vehicle count for that trajectory. ; In terms of pedestrian and bicycle counting, the system is capable of tracking these “objects” as well, ; though at present it is not capable of distinguishing between the three classes automatically. ; Continuing research by the principal investigator under a different grant will establish this capability as ; well.
  More +
• Format:
  PDF
• Funding:
  R27-131
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/28803/dot_28803_DS1.pdf
• File Type:
  [PDF - 3.31 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:5e3c12b951a6e4fc7936be31c26c818f8f503193a2894df92fe377bc5043830e