Final Report on Video Log Data Mining Project

Details

• Creators:
  Krishnan, Anupama ; Lewis, Chris ; Day, Dwight
• Corporate Creators:
  Kansas State University. Dept. of Electrical and Computer Engineering
• Corporate Contributors:
  Kansas. Dept. of Transportation. Bureau of Materials & Research
• Subject/TRT Terms:
  Data Mining Digital Video Road Profiling Sign Inventory Traffic Signs Video Cameras Video Log
• Publication/ Report Number:
  KS-09-9
• Resource Type:
  Tech Report
• Geographical Coverage:
  Kansas
• Edition:
  Final report; July 2008¿Sept. 2009.
• Corporate Publisher:
  Kansas. Dept. of Transportation
• Abstract:
  This report describes the development of an automated computer vision system that identities and inventories road signs
  
  from imagery acquired from the Kansas Department of Transportation’s road profiling system that takes images every 26.4
  
  feet on highways throughout the state. Statistical models characterizing the typical size, color, and physical location of signs
  
  are used to help identify signs from the imagery. First, two phases of a computationally efficient K-Means clustering
  
  algorithm are applied to the images to achieve over-segmentation. The novel second phase ensures over-segmentation
  
  without excessive computation. Extremely large and very small segments are rejected. The remaining segments are then
  
  classified based on color. Finally, the frame to frame trajectories of sign colored segments are analyzed using triangulation
  
  and Bundle adjustment to determine their physical location relative to the road profiler. Objects having the appropriate color,
  
  and physical placement are entered into a sign database. To develop the statistical models used for classification, a
  
  representative set of images was segmented and manually labeled determining the joint probabilistic models characterizing
  
  the color and location typical to that of road signs. Receiver Operating Characteristic curves were generated and analyzed to
  
  adjust the thresholds for the class identification. This system was tested and its performance characteristics are presented.
  
  
• Format:
  PDF
• Funding:
  C1809
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:b476a9587fdc4dee97d9bfd28b229dd38f89c32b16b509d9d78bc42a299496c6
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24976/dot_24976_DS1.pdf
• File Type:
  [PDF - 3.15 MB ]