Abstract—This paper presents a novel method for background estimation in a video sequence from the function estimation point of view. The proposed algorithm, called Kernel-based Background Learning (KBL), is designed based on kernel machine joint with learning schemes. In order to estimate background using KBL algorithm, we first interpret foreground samples as outliers relative to the background ones and so propose an Outlier Separator (OS). Then, the obtained results of OS algorithm are employed in the KBL method in order to train and estimate background in each pixel. Experimental results show the high accuracy and effectiveness of the proposed method in background estimation and foreground detection for the scenes including moving backgrounds, camera shakes, and non-empty backgrounds.
Index Terms—Background estimation, outlier separator, kernel-based background learning.
Hamidreza Baradaran Kashani is with the Electrical Department, Ferdowsi University of Mashhad, Iran. Postal Code: 9177948944, P.O. Box:1111, fax:+985118763301 (email: hamidreza.baradaran@gmail.com).
Seyed Alireza Seyedin is with the Electrical Department, Ferdowsi University of Mashhad, Iran. Postal Code: 9177948944, P.O. Box:1111, fax:+985118763301(email: seyedin@um.ac.ir).
Hadi Sadoghi Yazdi is with the Computer Department, Ferdowsi University of Mashhad, Iran. Postal Code: 9177948944, P.O. Box:1111, fax:+985118763301 (email: h-sadoghi@um.ac.ir)
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Cite: Hamidreza Baradaran Kashani, Seyed Alireza Seyedin and Hadi Sadoghi Yazdi, "A Novel Approach in Video Scene Background Estimation,"
International Journal of Computer Theory and Engineering vol. 2, no. 2, pp. 274-282, 2010.
