Towards efficient vehicle classification in intelligent transportation systems

Abdul Jabbar Siddiqui; Abdelhamid Mammeri; Azzedine Boukerche

doi:10.1145/2815347.2815354

Towards efficient vehicle classification in intelligent transportation systems

Abdul Jabbar Siddiqui
, Abdelhamid Mammeri
, Azzedine Boukerche

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

The classification of vehicles is an important task in Intelligent Transportation Systems (ITS) for applications such as analyzing traffic, checking for fraud, tracking targets, and other security applications. In the recent years, automated systems to recognize makes and models of oncoming vehicles are gaining attention, utilizing existing infrastructure of traffic cameras. To this end, we present an unexplored approach for vehicle make and model recognition (VMMR) and demonstrate its highly accurate and real-time performance, using a recently published real-world dataset. The encouraging results of our approach pave the way towards efficient large-scale and distributed vehicular surveillance in ITS.

Original language	English
Title of host publication	DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications
Publisher	Association for Computing Machinery, Inc
Pages	19-25
Number of pages	7
ISBN (Electronic)	9781450337601
DOIs	https://doi.org/10.1145/2815347.2815354
State	Published - 2 Nov 2015
Externally published	Yes

Publication series

Name	DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

Bibliographical note

Publisher Copyright:
© 2015 ACM.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Intelligent vehicles
Vehicle identification and classification
Vehicle make and model recognition

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence
Automotive Engineering
Control and Systems Engineering
Transportation

Access to Document

10.1145/2815347.2815354

Cite this

Siddiqui, A. J., Mammeri, A., & Boukerche, A. (2015). Towards efficient vehicle classification in intelligent transportation systems. In DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications (pp. 19-25). (DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications). Association for Computing Machinery, Inc. https://doi.org/10.1145/2815347.2815354

Siddiqui, Abdul Jabbar ; Mammeri, Abdelhamid ; Boukerche, Azzedine. / Towards efficient vehicle classification in intelligent transportation systems. DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications. Association for Computing Machinery, Inc, 2015. pp. 19-25 (DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications).

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title = "Towards efficient vehicle classification in intelligent transportation systems",

abstract = "The classification of vehicles is an important task in Intelligent Transportation Systems (ITS) for applications such as analyzing traffic, checking for fraud, tracking targets, and other security applications. In the recent years, automated systems to recognize makes and models of oncoming vehicles are gaining attention, utilizing existing infrastructure of traffic cameras. To this end, we present an unexplored approach for vehicle make and model recognition (VMMR) and demonstrate its highly accurate and real-time performance, using a recently published real-world dataset. The encouraging results of our approach pave the way towards efficient large-scale and distributed vehicular surveillance in ITS.",

keywords = "Intelligent vehicles, Vehicle identification and classification, Vehicle make and model recognition",

author = "Siddiqui, \{Abdul Jabbar\} and Abdelhamid Mammeri and Azzedine Boukerche",

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year = "2015",

month = nov,

day = "2",

doi = "10.1145/2815347.2815354",

language = "English",

series = "DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications",

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}

Siddiqui, AJ, Mammeri, A & Boukerche, A 2015, Towards efficient vehicle classification in intelligent transportation systems. in DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications. DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, Association for Computing Machinery, Inc, pp. 19-25. https://doi.org/10.1145/2815347.2815354

Towards efficient vehicle classification in intelligent transportation systems. / Siddiqui, Abdul Jabbar; Mammeri, Abdelhamid; Boukerche, Azzedine.
DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications. Association for Computing Machinery, Inc, 2015. p. 19-25 (DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Towards efficient vehicle classification in intelligent transportation systems

AU - Siddiqui, Abdul Jabbar

AU - Mammeri, Abdelhamid

AU - Boukerche, Azzedine

PY - 2015/11/2

Y1 - 2015/11/2

N2 - The classification of vehicles is an important task in Intelligent Transportation Systems (ITS) for applications such as analyzing traffic, checking for fraud, tracking targets, and other security applications. In the recent years, automated systems to recognize makes and models of oncoming vehicles are gaining attention, utilizing existing infrastructure of traffic cameras. To this end, we present an unexplored approach for vehicle make and model recognition (VMMR) and demonstrate its highly accurate and real-time performance, using a recently published real-world dataset. The encouraging results of our approach pave the way towards efficient large-scale and distributed vehicular surveillance in ITS.

AB - The classification of vehicles is an important task in Intelligent Transportation Systems (ITS) for applications such as analyzing traffic, checking for fraud, tracking targets, and other security applications. In the recent years, automated systems to recognize makes and models of oncoming vehicles are gaining attention, utilizing existing infrastructure of traffic cameras. To this end, we present an unexplored approach for vehicle make and model recognition (VMMR) and demonstrate its highly accurate and real-time performance, using a recently published real-world dataset. The encouraging results of our approach pave the way towards efficient large-scale and distributed vehicular surveillance in ITS.

KW - Intelligent vehicles

KW - Vehicle identification and classification

KW - Vehicle make and model recognition

UR - https://www.scopus.com/pages/publications/84959296950

U2 - 10.1145/2815347.2815354

DO - 10.1145/2815347.2815354

M3 - Conference contribution

AN - SCOPUS:84959296950

T3 - DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

SP - 19

EP - 25

BT - DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

PB - Association for Computing Machinery, Inc

ER -

Siddiqui AJ, Mammeri A, Boukerche A. Towards efficient vehicle classification in intelligent transportation systems. In DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications. Association for Computing Machinery, Inc. 2015. p. 19-25. (DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications). doi: 10.1145/2815347.2815354

Towards efficient vehicle classification in intelligent transportation systems

Abstract

Publication series

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this