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

Huynh, Nathan N.; Mullen, Robert L; Mejia, Yohanna; University of South Carolina. Dept. of Civil & Environmental Engineering

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

2019-03-01
By Huynh, Nathan N. ; Mullen, Robert L ; Mejia, Yohanna

[PDF-1.94 MB]

English

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Details:

Creators:

Huynh, Nathan N. ; Mullen, Robert L ; Mejia, Yohanna

Huynh, Nathan N. ; Mullen, Robert L ; Mejia, Yohanna Less -
Corporate Creators:

University of South Carolina. Dept. of Civil & Environmental Engineering
Corporate Contributors:

Center for Connected Multimodal Mobility, Clemson University
Subject/TRT Terms:

[+]

Cascade Classifiers Computer Vision Data Fusion Haar-like Features Image Analysis Local Binary Patterns Methodology Neural Networks Traffic Counts Transportation Modes Vehicle Classification Vehicle Detectors
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report January-December 2018
Corporate Publisher:

Center for Connected Multimodal Mobility, Clemson University
Abstract:

Several methodologies to count and classify vehicles from video recordings were explored in this research to provide transportation agencies with a safer alternative to collect these data which are needed for traffic engineering and planning studies. In this study, three approaches for detecting and classifying vehicles were investigated: Haar-like features, Local Binary Patterns (LBP), and Convolutional Neural Networks (CNN). Recordings from the South Carolina Department of Transportation (SCDOT) were used for the study. By means of image subtraction and contouring, specific areas of the video stream were extracted as independent images. The resulting database was used for training and testing the Cascade classifiers and the CNN. Results indicate that using a data set consisting of 5,500 car and 2,800 truck images, the CNN achieved higher accuracy (97%) in vehicle classification compared to LBP and Haar cascade.
Format:

PDF
Funding:

69A3551747117
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:ad9056834392af76bbaf0898a9850b13db550dd15b5992ed5b0fc0a600111701
Download URL:

https://rosap.ntl.bts.gov/view/dot/53604/dot_53604_DS1.pdf
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Real Time Classification of Vehicle Types and Modes using Image Analysis and Data Fusion

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