A Nonparametric Approach to Foreground Detection in Dynamic Backgrounds

Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel v...

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Bibliographic Details
Published inChina communications Vol. 12; no. 2; pp. 32 - 39
Main Authors Liao, Juan, Jiang, Dengbiao, Li, Bo, Ruan, Yaduan, Chen, Qimei
Format Journal Article
LanguageEnglish
Published China Institute of Communications 01.02.2015
Institute of Electronic Science and Engineering, Nanjing, Nanjing 210046, Jiangsu Province, China
Subjects
Adaptation models
Computational modeling
dynamic background
Euclidean distance
Feature extraction
foreground detection
Probability density function
Robustness
Spatial coherence
the decision threshold
动态背景
历史记录
空间相干性
背景模型
自适应阈值
视频监控
错误检测
非参数方法
foreground detection
spatial coherence
the decision threshold
dynamic background
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Summary:Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel values to estimate background model.Besides,the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections.Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.
Bibliography:foreground detection dynamic background the decision threshold spatial coherence
Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel values to estimate background model.Besides,the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections.Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.
11-5439/TN
ISSN:1673-5447
DOI:10.1109/CC.2015.7084400