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:
  • Digital video
  • Video cameras
  • Data mining
  • Traffic signs
  • Sign inventory
  • Road profiling
  • Video log
  • Data mining
• 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.
  More +
• Format:
  PDF
• Funding:
  C1809
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24976/dot_24976_DS1.pdf
• File Type:
  [PDF - 3.15 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:b476a9587fdc4dee97d9bfd28b229dd38f89c32b16b509d9d78bc42a299496c6