Real Time Classification of Vehicle Types and Modes using Image Analysis and Data Fusion

Description: The goal of this project is to conduct a feasibility study on the development of software and selection of hardware that will measure multiple transportation modes and classify vehicles by their Federal Highway Administration (FHWA) classification. The research team will install several combined computer/camera systems to monitor the multi-modal traffic in the proximity of the University of South Carolina campus. This area has multiple transportation users, including pedestrians, mopeds, bicycles, motorcycles, passenger cars, trucks, trains and buses. Along with the video data, additional traffic collection sources such as pneumatic tubes and Bluetooth will be used. Multiple cameras will allow three dimensional data of the environment to be constructed in the software. The video data will be combined with other data using statistical updating methods (Bayesian) to produce final multi-modal traffic information. We will also explore counting traffic in non-typical locations, such as counting the number of pedestrians in/outside of cross walks in the roadway or pedestrians crossing stopped trains. Intellectual Merit: (1) Image subtractions from successive images will be used to identify objects in the area of interest. (2) A discriminate function based on the object geometry and image texture will be used to classify objects. (3) The development of the object discriminant function as well as utilization of digital image correlation or other video object motion determination approaches will be the major contribution of this research. Broader Impacts: Broader Impacts: The collection and analysis of integrated multimodal movement of people and goods will provide transportation planners with better quantitative information about the existing system. Beyond providing raw counts, an integrated video based system could provide information about unsafe practices of pedestrians and moped users that could be used to improve safety for these users.

Language

• English

Project

• Status: Completed
• Funding: $75085
• Contract Numbers:

  69A3551747117

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  University of South Carolina, Columbia

  502 Byrnes Building
  Columbia, SC  United States  29208
• Managing Organizations:

  Center for Connected Multimodal Mobility

  Clemson University
  Clemson, SC  United States  29634
• Project Managers:

  Mullen, Robert L.

• Performing Organizations:

  University of South Carolina, Columbia

  502 Byrnes Building
  Columbia, SC  United States  29208

  Clemson University

  110 Lowry Hall
  Box 340911
  Clemson, SC  United States  29634-0911
• Principal Investigators:

  Mullen, Robert

  Huynh, Nathan

• Start Date: 20171002
• Expected Completion Date: 20190430
• Actual Completion Date: 20190430
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Computer vision; Data fusion; Image analysis; Neural networks; Traffic counts; Vehicle classification
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01913006
• Record Type: Research project
• Source Agency: Center for Connected Multimodal Mobility
• Contract Numbers: 69A3551747117
• Files: UTC, RIP
• Created Date: Mar 25 2024 3:48PM