Foreground Segmentation for Moving Cameras under Low Illumination Conditions

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

2016

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.

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