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Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets.

Amr Yousef1,2, Jeff Flora3, Khan Iftekharuddin3

  • 1Engineering Mathematics Department, Alexandria University, Lotfy El-Sied st. off Gamal Abd El-Naser, Alexandria 11432, Egypt.

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|November 11, 2022
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Summary
This summary is machine-generated.

This study introduces a view-angle independent computer vision system for vehicle segmentation and classification from traffic videos. The new method enhances accuracy and processing speed, outperforming deep learning approaches.

Keywords:
HOGKalman filterSVMdeep learning architectureslow-rankmatrix decompositionvehicle segmentation and classification

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Traffic Engineering

Background:

  • Roadway traffic monitoring is crucial for transportation management.
  • Existing vehicle segmentation and classification methods face challenges with varying view angles and processing speed.

Purpose of the Study:

  • To develop a view-angle independent computer vision framework for vehicle segmentation and classification.
  • To improve the accuracy and efficiency of traffic analysis using Virginia Department of Transportation (VDOT) data.

Main Methods:

  • Implemented an automated region of interest extraction for faster processing.
  • Utilized improved robust low-rank matrix decomposition for vehicle segmentation.
  • Employed Histogram of Oriented Gradients (HOG) and morphological properties for feature extraction.
  • Used a multi-class support vector machine (SVM) classifier for vehicle categorization.
  • Applied iterative k-means clustering for handling multiple vehicle detections.

Main Results:

  • Achieved an average reduction of 40% in processed data.
  • Demonstrated an average improvement of 15% in segmentation accuracy compared to recent techniques.
  • Showed an average speed increase of 55% over compared segmentation methods.
  • Outperformed 23 deep learning architectures in vehicle classification accuracy.
  • Confirmed real-time operational capability through timing analysis.

Conclusions:

  • The developed framework offers a robust and efficient solution for vehicle segmentation and classification.
  • The system's view-angle independence and speed improvements make it suitable for real-world traffic monitoring applications.
  • This approach surpasses current deep learning methods in accuracy and real-time performance for traffic analysis.