Recursive Kernel Density Estimation for modeling the background and segmenting moving objects

Zhu, Qingsong, Shao, Ling, Li, Qi and Xie, Yaoqin (2013) Recursive Kernel Density Estimation for modeling the background and segmenting moving objects. In: ICASSP 2013 - International Conference on Acoustics, Speech and Signal Processing, 26th - 31st May 2013, Vancouver, Canada.

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Abstract

Identifying moving objects in a video sequence is a fundamental and critical task in video surveillance, traffic monitoring, and gesture recognition in human-machine interface. In this paper, we present a novel recursive Kernel Density Estimation based background modeling method. First, local maximum in the density functions is recursively approximated using a mean shift method. Second, components and parameters in the mixture Gaussian distributions can be selected adaptively via a proposed thresholding mechanism, and finally converge to a stable background distribution model. In the scene segmentation, foreground is firstly separated by simple background subtraction approach. And then a local texture correlation operator is introduced to fill the vacancies and remove the fractional false foreground regions so as to obtain a better video segmentation quality. Experiments conducted on synthetic and video data demonstrate the superior performance of the proposed algorithms.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Recursive Kernel Density Estimation, background modeling, video segmentation
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	16 Jun 2015 13:35
Last Modified:	13 Oct 2019 00:33
URI:	http://nrl.northumbria.ac.uk/id/eprint/22952

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