Non-parametric statistical background modeling for efficient foreground region detection |
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Authors: | Alireza Tavakkoli Mircea Nicolescu George Bebis Monica Nicolescu |
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Affiliation: | 1. Computer Vision Lab, University of Nevada, Reno, USA 2. Robotics Laboratory, University of Nevada, Reno, USA
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Abstract: | Most methods for foreground region detection in videos are challenged by the presence of quasi-stationary backgrounds—flickering
monitors, waving tree branches, moving water surfaces or rain. Additional difficulties are caused by camera shake or by the
presence of moving objects in every image. The contribution of this paper is to propose a scene-independent and non-parametric
modeling technique which covers most of the above scenarios. First, an adaptive statistical method, called adaptive kernel
density estimation (AKDE), is proposed as a base-line system that addresses the scene dependence issue. After investigating
its performance we introduce a novel general statistical technique, called recursive modeling (RM). The RM overcomes the weaknesses
of the AKDE in modeling slow changes in the background. The performance of the RM is evaluated asymptotically and compared
with the base-line system (AKDE). A wide range of quantitative and qualitative experiments is performed to compare the proposed
RM with the base-line system and existing algorithms. Finally, a comparison of various background modeling systems is presented
as well as a discussion on the suitability of each technique for different scenarios. |
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Keywords: | |
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