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Vanishing point detection for visual surveillance systems in railway platform environments

Tarrit, Katy; Molleda, Julio; Atkinson, Gary A.; Smith, Melvyn L.; Wright, Glynn C.; Gaal, Peter

Authors

Katy Tarrit

Julio Molleda

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Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof

Glynn C. Wright

Peter Gaal



Abstract

© 2018 Elsevier B.V. Visual surveillance is of paramount importance in public spaces and especially in train and metro platforms which are particularly susceptible to many types of crime from petty theft to terrorist activity. Image resolution of visual surveillance systems is limited by a trade-off between several requirements such as sensor and lens cost, transmission bandwidth and storage space. When image quality cannot be improved using high-resolution sensors, high-end lenses or IR illumination, the visual surveillance system may need to increase the resolving power of the images by software to provide accurate outputs such as, in our case, vanishing points (VPs). Despite having numerous applications in camera calibration, 3D reconstruction and threat detection, a general method for VP detection has remained elusive. Rather than attempting the infeasible task of VP detection in general scenes, this paper presents a novel method that is fine-tuned to work for railway station environments and is shown to outperform the state-of-the-art for that particular case. In this paper, we propose a three-stage approach to accurately detect the main lines and vanishing points in low-resolution images acquired by visual surveillance systems in indoor and outdoor railway platform environments. First, several frames are used to increase the resolving power through a multi-frame image enhancer. Second, an adaptive edge detection is performed and a novel line clustering algorithm is then applied to determine the parameters of the lines that converge at VPs; this is based on statistics of the detected lines and heuristics about the type of scene. Finally, vanishing points are computed via a voting system to optimize detection in an attempt to omit spurious lines. The proposed approach is very robust since it is not affected by ever-changing illumination and weather conditions of the scene, and it is immune to vibrations. Accurate and reliable vanishing point detection provides very valuable information, which can be used to aid camera calibration, automatic scene understanding, scene segmentation, semantic classification or augmented reality in platform environments.

Citation

Tarrit, K., Molleda, J., Atkinson, G. A., Smith, M. L., Wright, G. C., & Gaal, P. (2018). Vanishing point detection for visual surveillance systems in railway platform environments. Computers in Industry, 98, 153-164. https://doi.org/10.1016/j.compind.2018.03.005

Journal Article Type Article
Acceptance Date Mar 12, 2018
Publication Date Jun 1, 2018
Deposit Date Mar 13, 2018
Publicly Available Date Sep 2, 2019
Journal Computers in Industry
Print ISSN 0166-3615
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 98
Pages 153-164
DOI https://doi.org/10.1016/j.compind.2018.03.005
Keywords visual surveillance, image registration, image reconstruction, adaptive edge
detection; vanishing point detection.
Public URL https://uwe-repository.worktribe.com/output/876092
Publisher URL https://doi.org/10.1016/j.compind.2018.03.005

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