A model for dynamic object segmentation with kernel density estimation based on gradient features

Authors:
Zheng Li;Pohuang Jiang;Hong Ma;Jian Yang;DongMing Tang
Affiliations:
College of Computer Science, Sichuan University, Chengdu, Sichuan 610064, China;College of Computer Science, Sichuan University, Chengdu, Sichuan 610064, China;College of Mathematics, Sichuan University, Chengdu, Sichuan 610064, China;University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China;University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
Venue:
Image and Vision Computing
Year:
2009

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Abstract

The dynamic object segmentation in videos taken from a static camera is a basic technique in many vision surveillance applications. In order to suppress fake objects caused by dynamic cast shadows and reflection images, this paper presents a novel segmentation model with the function of cast shadow and reflection image suppression. This model is a kernel density estimation model based on dynamic gradient features. Unlike the conventional kernel density estimation model which can only suppress cast shadows in color videos, this model can also suppress them in intensity videos, and under the circumstance of diffusion it can suppress reflection images effectively. Although this model may cause the increase of the false negative rate, its function of fake object suppression is remarkable. Furthermore, the false negative rate can be reduced with other convenient methods. Some experimental results by real videos are also presented in this paper to demonstrate the effectiveness of this model.