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Vehicle Make and Model Recognition using Bag of Expressions.

Adeel Ahmad Jamil1, Fawad Hussain1, Muhammad Haroon Yousaf1,2

  • 1Centre for Computer Vision Research, Department of Computer Engineering, University of Engineering and Technology, Taxila 47050, Pakistan.

Sensors (Basel, Switzerland)
|February 21, 2020
PubMed
Summary

The new Bag of Expressions (BoE) method enhances vehicle make and model recognition (VMMR) by incorporating neighborhood information. This approach improves upon existing methods, offering better performance for automated surveillance and intelligent transport systems.

Keywords:
bag of expressionsintelligent transportationmake and model recognitionmulticlass linear support vector machinesvehicular surveillance.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Intelligent Transport Systems

Background:

  • Vehicle make and model recognition (VMMR) is crucial for automated vehicular surveillance (AVS) and intelligent transport systems (ITS).
  • Existing methods like Bag of Words (BoW) have limitations in handling real-world variations.
  • There is a need for robust VMMR techniques that can manage occlusion, scale, and view variations.

Purpose of the Study:

  • To propose and evaluate the suitability of the Bag of Expressions (BoE) approach for VMMR applications.
  • To enhance VMMR performance by integrating neighborhood information with visual words.
  • To demonstrate the effectiveness of BoE in improving occlusion handling, scale invariance, and view independence.

Main Methods:

  • Feature extraction using a combination of keypoint detectors and Histogram of Oriented Gradients (HOG) descriptor.
  • Formation of an optimized dictionary of expressions via k-means clustering of visual words.
  • Classification using multiclass linear Support Vector Machines (SVM) trained on BoE features.

Main Results:

  • The BoE approach demonstrated superior performance compared to recent VMMR methods on the NTOU-MMR dataset.
  • Achieved promising average accuracy and processing speed, indicating real-time applicability.
  • The integration of neighborhood information significantly improved recognition capabilities.

Conclusions:

  • The Bag of Expressions (BoE) approach is highly suitable for vehicle make and model recognition (VMMR) tasks.
  • BoE offers significant advantages over traditional methods, particularly in challenging conditions.
  • The proposed method is a viable solution for real-time VMMR systems in intelligent transport and surveillance.