Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147723
Title: Rapid and robust background modeling technique for low-cost road traffic surveillance systems
Authors: Garg, Kratika
Ramakrishnan, Nirmala
Prakash, Alok
Srikanthan, Thambipillai
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: 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
Project: NRF TUMCREATE
Journal: IEEE Transactions on Intelligent Transportation Systems
Abstract: 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.
URI: https://hdl.handle.net/10356/147723
ISSN: 1558-0016
DOI: 10.1109/TITS.2019.2917560
Rights: © 2020 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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