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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

A Comparative Study of Vehicle Detection Methods in a Video Sequence

verfasst von : Ameni Chetouane, Sabra Mabrouk, Imen Jemili, Mohamed Mosbah

Erschienen in: Distributed Computing for Emerging Smart Networks

Verlag: Springer International Publishing

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Abstract

Vehicle detection plays a significant role in traffic monitoring. Vehicle detection approaches can be used for vehicle tracking, vehicle classification and traffic analysis. However, numerous attributes like shape, intensity, size, pose, illumination, shadows, occlusion, velocity of vehicles and environmental conditions, provide different challenges for the detection step. With an appropriate vehicle detection technique, we are able to extract valuable knowledge from video sequences, regardless these diverse factors. Since the vehicle detection method choice has a deep impact on this step and the whole traffic monitoring system performances, our objective in this study is to investigate different methods for vehicle detection. Comparison is made on the basis of different metrics such as recall, precision and detection accuracy. These approaches have been tested under different weather conditions (rainy, sunny) and various traffic conditions (light, medium, heavy).

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Vorheriges Kapitel A Systematic Literature Review of Studies on Road Congestion Modelling
Nächstes Kapitel Energy Efficient Handshake Algorithm for Wireless Sensor Networks
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Metadaten
Titel
A Comparative Study of Vehicle Detection Methods in a Video Sequence
verfasst von
Ameni Chetouane
Sabra Mabrouk
Imen Jemili
Mohamed Mosbah
Copyright-Jahr
2020
Buch
Distributed Computing for Emerging Smart Networks

Print ISBN: 978-3-030-40130-6

Electronic ISBN: 978-3-030-40131-3

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-40131-3_3