Real-Time Traffic Analytics at Intersections

Details

• Creators:
  Narasimhan, Srinivasa G. ; Tamburo, Robert ; Narapureddy, Dinesh
• Corporate Creators:
  Mobility21, Carnegie Mellon University
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Computer vision
  • Pedestrian detectors
  • Real time information
  • Spatial analysis
  • Traffic surveillance
  • Vehicle trajectories
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Mobility21, Carnegie Mellon University
• Abstract:
  More and more city planners are taking into consideration smart mobility solutions to address transportation needs and central to smart transportation systems is access to real-time data. For example, self-driving cars offer independence for seniors and people with disabilities, greater road safety, cost savings through ride sharing, increased productivity, reduced congestion, and reduced fuel use and carbon emissions. But what type of information and data is needed for city planners to accommodate smart transportation systems? Visual data is extremely rich in information and algorithms can process the data and extract the information. However, bandwidth is limited and too much time is needed to transfer visual data to remote computers for analysis. This work focused on developing computer vision algorithms for analyzing visual data and computing and sharing the resulting analytics and summary data in real-time. Algorithms were developed to understand vehicle motion in 3D space and time, and to track the pose of people in 3D. These algorithms are vital to computing analytics on real data in the presence of occlusions, cluttered scenes, and varied lighting conditions.
• Format:
  PDF
• Funding:
  69A3551747111 ; PROJECT #335
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/60066/dot_60066_DS1.pdf
• File Type:
  [PDF - 6.81 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:f7cbaf260e3afb4de0d79c4a625634ac80e5734eb467b578cdf07d700f207be2a4f02d3fae00141d5a45d8dee315bd7963b6de7aab075fea40e000bdf3f05371