A Fast Foreground Object Detection Algorithm Using Kernel Density Estimation

被引：0
|
作者：
Li, Dawei [1 ]
Xu, Lihong [1 ]
Goodman, Erik [2 ]
机构：
[1] Tongji Univ, Dept Control Sci & Engn, Shanghai 200092, Peoples R China
[2] Michigan State Univ, Beacon Ctr, Dept Elect & Comp Engn, E Lansing, MI 48824 USA
来源：
PROCEEDINGS OF 2012 IEEE 11TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP) VOLS 1-3 | 2012年
基金：
美国国家科学基金会; 中国国家自然科学基金;
关键词：
foreground detection; kernel density estimation; markov random field; noise reduction; simulated annealing;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
a real-time foreground moving object detection algorithm based on Kernel Density Estimation in srgb color space is proposed in this paper, followed by an iterative noise reduction procedure, which removes dispersed noise and enhances the foreground contour in a binary image mask within a Bayesian framework. A simulated annealing strategy is applied in MRF-type decision making at the texture level to accelerate the convergence of the iterative noise reduction procedure. Experiments show that the proposed algorithm can resist undesirable effects of changing environmental illumination and shadow. Compared to several classical methods, better detection results are achieved on various datasets including both indoor and outdoor cases.
引用
收藏
页码：703 / +
页数：2
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