Adaptive video-based vehicle classification technique for monitoring traffic.

Details

• Creators:
  Wei, Heng ; Abrishami, Hedayat ; Xiao, Xinhua ; Karteek, Allam
• Corporate Creators:
  University of Cincinnati. Dept. of Civil/Architectural Enginerring and Construction Management
• Corporate Contributors:
  United States. Federal Highway Administration
• Subject/TRT Terms:
  • Vehicle classification
  • Vehicle length
  • Axles
  • Video
  • Image processing
  • Machine vision
  • Automatic vehicle identification
  • Traffic surveillance
• Publication/ Report Number:
  FHWA/OH-2015/20
• Resource Type:
  Tech Report
• Geographical Coverage:
  Ohio
• Corporate Publisher:
  Ohio. Dept. of Transportation. Office of Statewide Planning and Research
• Abstract:
  This report presents a methodology for extracting two vehicle features, vehicle length and number of axles in order ; to classify the vehicles from video, based on Federal Highway Administration (FHWA)’s recommended vehicle ; classification scheme. There are two stages regarding this classification. The first stage is the general classification ; that basically classifies vehicles into 4 categories or bins based on the vehicle length (i.e., 4-Bin length-based vehicle ; classification). The second stage is the axle-based group classification that classifies vehicles in more detailed ; classes of vehicles such as car, van, buses, based on the number of axles. The Rapid Video-based Vehicle ; Identification System (RVIS) model is developed based on image processing technique to enable identifying the ; number of vehicle axles. Also, it is capable of tackling group classification of vehicles that are defined by axles and ; vehicle length based on the FHWA’s vehicle classification scheme and standard lengths of 13 categorized vehicles. ; The RVIS model is tested with sample video data obtained on a segment of I-275 in the Cincinnati area, Ohio. The ; evaluation result shows a better 4-Bin length–based classification than the axle-based group classification. There ; may be two reasons. First, when a vehicle gets misclassified in 4-Bin classification, it will definitely be misclassified ; in axle-based group classification. The error of the 4-Bin classification will propagate to the axle-based group ; classification. Second, there may be some noises in the process of finding the tires and number of tires. The project ; result provides solid basis for integrating the RVIS that is particularly applicable to light traffic condition and the ; Vehicle Video-Capture Data Collector (VEVID), a semi-automatic tool to be particularly applicable to heavy traffic ; conditions, into a “hybrid” system in the future. Detailed framework and operation scheme for such an integration ; effort is provided in the project report.
  More +
• Format:
  PDF
• Funding:
  SJN 134874
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29363/dot_29363_DS1.pdf
• File Type:
  [PDF - 3.16 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:43ce4d2e3cd649457e397c34cca8dac9f535da8b96c8450c60e20d5e03f5a938