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Open AccessArticle

Vehicle Make and Model Recognition using Bag of Expressions

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Centre for Computer Vision Research, Department of Computer Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
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Swarm Robotics Lab, National Centre for Robotics and Automation,University of Engineering and Technology, Taxila 47050, Pakistan
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School of Electronic Engineering and Computer Science, Queen Mary University of London, E1 4NS, UK
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Zebra Technologies Corp., London SE1 9LQ, UK
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Department of Computer Science and Engineering, University Carlos III de Madrid, Colmenarejo 28270, Spain
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Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1033; https://doi.org/10.3390/s20041033
Received: 22 December 2019 / Revised: 8 February 2020 / Accepted: 11 February 2020 / Published: 14 February 2020
(This article belongs to the Section Intelligent Sensors)
Vehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.
Keywords: bag of expressions; intelligent transportation; make and model recognition; multiclass linear support vector machines; vehicular surveillance. bag of expressions; intelligent transportation; make and model recognition; multiclass linear support vector machines; vehicular surveillance.
MDPI and ACS Style

Jamil, A.A.; Hussain, F.; Yousaf, M.H.; Butt, A.M.; Velastin, S.A. Vehicle Make and Model Recognition using Bag of Expressions. Sensors 2020, 20, 1033.

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