Foreground Segmentation for Moving Cameras under Low Illumination Conditions

Wei Wang, Weili Li, Xiaoqing Yin, Yu Liu, Maojun Zhang

Abstract

A foreground segmentation method, including image enhancement, trajectory classification and object segmentation, is proposed for moving cameras under low illumination conditions. Gradient-field-based image enhancement is designed to enhance low-contrast images. On the basis of the dense point trajectories obtained in long frames sequences, a simple and effective clustering algorithm is designed to classify foreground and background trajectories. By combining trajectory points and a marker-controlled watershed algorithm, a new type of foreground labeling algorithm is proposed to effectively reduce computing costs and improve edge-preserving performance. Experimental results demonstrate the promising performance of the proposed approach compared with other competing methods.

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Paper Citation


in Harvard Style

Wang W., Li W., Yin X., Liu Y. and Zhang M. (2016). Foreground Segmentation for Moving Cameras under Low Illumination Conditions . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 65-71. DOI: 10.5220/0005695100650071


in Bibtex Style

@conference{icpram16,
author={Wei Wang and Weili Li and Xiaoqing Yin and Yu Liu and Maojun Zhang},
title={Foreground Segmentation for Moving Cameras under Low Illumination Conditions},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={65-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005695100650071},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Foreground Segmentation for Moving Cameras under Low Illumination Conditions
SN - 978-989-758-173-1
AU - Wang W.
AU - Li W.
AU - Yin X.
AU - Liu Y.
AU - Zhang M.
PY - 2016
SP - 65
EP - 71
DO - 10.5220/0005695100650071