Rapid and robust background modeling technique for low-cost road traffic surveillance systems

Fast and accurate detection of vehicles on road traffic scenes captured by traffic surveillance cameras, is essential for large-scale deployment of automated traffic surveillance systems. The state-of-the-art techniques typically employ background modeling for low-complexity foreground detection. Ho...

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Main Authors: Garg, Kratika, Ramakrishnan, Nirmala, Prakash, Alok, Srikanthan, Thambipillai
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/147723
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1477232021-04-12T09:21:54Z Rapid and robust background modeling technique for low-cost road traffic surveillance systems Garg, Kratika Ramakrishnan, Nirmala Prakash, Alok Srikanthan, Thambipillai School of Computer Science and Engineering Engineering::Computer science and engineering Intelligent Transport Systems Adaptation Models Fast and accurate detection of vehicles on road traffic scenes captured by traffic surveillance cameras, is essential for large-scale deployment of automated traffic surveillance systems. The state-of-the-art techniques typically employ background modeling for low-complexity foreground detection. However, this is a challenging problem as these methods need to be robust to varying road scene conditions (such as illumination changes, camera jitter, stationary vehicles, and heavy traffic) leading to huge computation cost. In this paper, we propose a highly accurate yet low-complexity foreground (i.e., vehicle) detection technique, which can effectively deal with the varying road scene conditions, and generate accurate pixel-level foreground masks in real-time. We propose a novel robust block-based feature suitable for modeling road background and detecting vehicles as foreground, and employ Bayesian probabilistic modeling on these features. The experimental evaluations on widely used traffic datasets demonstrate that the proposed method can achieve comparable accuracy to the existing state-of-the-art techniques but at a much higher processing frame rate (40x speedup over PAWCS). The real-time performance of the proposed system has also been demonstrated by implementing it on a low-cost embedded platform, Odroid XU-4, that still achieves a frame rate of over 80 frames/s, thereby enabling the real-time detection of foreground objects in road scenes. 2021-04-12T09:21:54Z 2021-04-12T09:21:54Z 2020 Journal Article Garg, K., Ramakrishnan, N., Prakash, A. & Srikanthan, T. (2020). Rapid and robust background modeling technique for low-cost road traffic surveillance systems. IEEE Transactions On Intelligent Transportation Systems, 21(5), 2204-2215. https://dx.doi.org/10.1109/TITS.2019.2917560 1558-0016 0000-0003-3937-4395 0000-0001-8257-2974 https://hdl.handle.net/10356/147723 10.1109/TITS.2019.2917560 2-s2.0-85084472097 5 21 2204 2215 en NRF TUMCREATE IEEE Transactions on Intelligent Transportation Systems © 2020 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Intelligent Transport Systems
Adaptation Models
spellingShingle Engineering::Computer science and engineering
Intelligent Transport Systems
Adaptation Models
Garg, Kratika
Ramakrishnan, Nirmala
Prakash, Alok
Srikanthan, Thambipillai
Rapid and robust background modeling technique for low-cost road traffic surveillance systems
description Fast and accurate detection of vehicles on road traffic scenes captured by traffic surveillance cameras, is essential for large-scale deployment of automated traffic surveillance systems. The state-of-the-art techniques typically employ background modeling for low-complexity foreground detection. However, this is a challenging problem as these methods need to be robust to varying road scene conditions (such as illumination changes, camera jitter, stationary vehicles, and heavy traffic) leading to huge computation cost. In this paper, we propose a highly accurate yet low-complexity foreground (i.e., vehicle) detection technique, which can effectively deal with the varying road scene conditions, and generate accurate pixel-level foreground masks in real-time. We propose a novel robust block-based feature suitable for modeling road background and detecting vehicles as foreground, and employ Bayesian probabilistic modeling on these features. The experimental evaluations on widely used traffic datasets demonstrate that the proposed method can achieve comparable accuracy to the existing state-of-the-art techniques but at a much higher processing frame rate (40x speedup over PAWCS). The real-time performance of the proposed system has also been demonstrated by implementing it on a low-cost embedded platform, Odroid XU-4, that still achieves a frame rate of over 80 frames/s, thereby enabling the real-time detection of foreground objects in road scenes.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Garg, Kratika
Ramakrishnan, Nirmala
Prakash, Alok
Srikanthan, Thambipillai
format Article
author Garg, Kratika
Ramakrishnan, Nirmala
Prakash, Alok
Srikanthan, Thambipillai
author_sort Garg, Kratika
title Rapid and robust background modeling technique for low-cost road traffic surveillance systems
title_short Rapid and robust background modeling technique for low-cost road traffic surveillance systems
title_full Rapid and robust background modeling technique for low-cost road traffic surveillance systems
title_fullStr Rapid and robust background modeling technique for low-cost road traffic surveillance systems
title_full_unstemmed Rapid and robust background modeling technique for low-cost road traffic surveillance systems
title_sort rapid and robust background modeling technique for low-cost road traffic surveillance systems
publishDate 2021
url https://hdl.handle.net/10356/147723
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