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Journal of Electronic Imaging • new

Fast background subtraction for moving cameras based on nonparametric models
Author(s): Feng Sun; Kaihuai Qin; Wei Sun; Huayuan Guo
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Paper Abstract

In this paper, a fast background subtraction algorithm for freely moving cameras is presented. A nonparametric sample consensus model is employed as the appearance background model. The as-similar-as-possible warping technique, which obtains multiple homographies for different regions of the frame, is introduced to robustly estimate and compensate the camera motion between the consecutive frames. Unlike previous methods, our algorithm does not need any preprocess step for computing the dense optical flow or point trajectories. Instead, a superpixel-based seeded region growing scheme is proposed to extend the motion cue based on the sparse optical flow to the entire image. Then, a superpixel-based temporal coherent Markov random field optimization framework is built on the raw segmentations from the background model and the motion cue, and the final background/foreground labels are obtained using the graph-cut algorithm. Extensive experimental evaluations show that our algorithm achieves satisfactory accuracy, while being much faster than the state-of-the-art competing methods.

Paper Details

Date Published: 9 June 2016
PDF: 10 pages
J. Electron. Imaging. 25(3) 033017 doi: 10.1117/1.JEI.25.3.033017
Published in: Journal of Electronic Imaging Volume 25, Issue 3
Show Author Affiliations
Feng Sun, Tsinghua Univ. (China)
Chinese Satellite Maritime Tracking and Control (China)
Kaihuai Qin, Tsinghua Univ. (China)
Wei Sun, Institute of Army Aviation (China)
Huayuan Guo, Tsinghua Univ. (China)

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