Fast background subtraction for moving cameras based on nonparametric models

Feng Sun; Kaihuai Qin; Wei Sun; Huayuan Guo

doi:10.1117/1.JEI.25.3.033017

9 June 2016 Fast background subtraction for moving cameras based on nonparametric models

Feng Sun, Kaihuai Qin, Wei Sun, Huayuan Guo

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 3, 033017 (June 2016). https://doi.org/10.1117/1.JEI.25.3.033017

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.

Citation Download Citation

Feng Sun, Kaihuai Qin, Wei Sun, and Huayuan Guo "Fast background subtraction for moving cameras based on nonparametric models," Journal of Electronic Imaging 25(3), 033017 (9 June 2016). https://doi.org/10.1117/1.JEI.25.3.033017

Published: 9 June 2016

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