基于魯棒主成分分析的運(yùn)動(dòng)目標(biāo)檢測(cè)優(yōu)化算法

楊依忠; 汪鵬飛; 胡雄樓; 伍能舉

doi:10.11999/JEIT170789

基于魯棒主成分分析的運(yùn)動(dòng)目標(biāo)檢測(cè)優(yōu)化算法

doi: 10.11999/JEIT170789

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61401137, 61404043)，安徽省科技重大專(zhuān)項(xiàng)(16030901007)，中央高?；A(chǔ)研究基金(J2014HGXJ0083)

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- 文章訪問(wèn)數(shù): 1640
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-08-04
- 修回日期: 2018-01-10
- 刊出日期: 2018-06-19

Moving Object Detection Optimization Algorithm Based on Robust Principal Component Analysis

Funds:

The National Natural Science Foundation of China (61401137, 61404043), The Key Science and Technology Project of Anhui Province (16030901007), The Fundamental Research Funds for the Central Universities (J2014HGXJ0083)

摘要

摘要: 針對(duì)魯棒主成分分析(Robust Principal Component Analysis, RPCA)算法中將動(dòng)態(tài)背景誤檢為運(yùn)動(dòng)目標(biāo)的問(wèn)題，該文提出一種運(yùn)動(dòng)目標(biāo)檢測(cè)優(yōu)化算法。在RPCA算法初步檢測(cè)出運(yùn)動(dòng)目標(biāo)后，利用動(dòng)態(tài)背景在時(shí)間域上滿足高斯分布的特性，以及動(dòng)態(tài)背景和運(yùn)動(dòng)目標(biāo)在整個(gè)視頻流上檢出點(diǎn)均值和方差的差異特性，進(jìn)一步將動(dòng)態(tài)背景和運(yùn)動(dòng)目標(biāo)分離開(kāi)來(lái)。實(shí)驗(yàn)結(jié)果表明，所提算法能夠有效地處理動(dòng)態(tài)背景的問(wèn)題，并在一定程度上完整檢測(cè)出運(yùn)動(dòng)目標(biāo)。
- 運(yùn)動(dòng)目標(biāo)檢測(cè) /
- 魯棒主成分分析 /
- 動(dòng)態(tài)背景 /
- 時(shí)間域
Abstract: Since dynamic background may be erroneously detected as a moving object in the Robust Principal Component Analysis (RPCA) algorithm, a RPCA-based moving object detection optimization algorithm is proposed to improve it. After detected by the RPCA algorithm, the moving object will be separated from dynamic background according to the Gaussian distribution of dynamic background in the time domain and the difference of mean value and variance between dynamic background and moving object in the whole video stream. The results show that the algorithm can deal with dynamic background effectively and detect the moving objects well.
- Moving object detection /
- Robust Principal Component Analysis (RPCA) /
- Dynamic background /
- Time domain

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參考文獻(xiàn)(21)

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