本文選題：魯棒PCA + 監(jiān)獄犯人越界檢測(cè)系統(tǒng)��； 參考：《國(guó)防科學(xué)技術(shù)大學(xué)》2016年碩士論文

【摘要】：隨著計(jì)算機(jī)智能技術(shù)迅速發(fā)展,安防系統(tǒng)向智能化方向發(fā)展開(kāi)始成為可能。隨著它的發(fā)展和應(yīng)用,人們無(wú)需肉眼緊盯視頻,避免長(zhǎng)時(shí)間工作導(dǎo)致的視覺(jué)性疲勞,從而杜絕監(jiān)控區(qū)域出現(xiàn)報(bào)警失誤,防止發(fā)生違法犯罪、事故案件。此外,安防系統(tǒng)能充分發(fā)揮計(jì)算機(jī)視覺(jué)技術(shù)在社會(huì)輿情監(jiān)控、智能交通等方面的重大作用。因而,如何運(yùn)用計(jì)算機(jī)視覺(jué)技術(shù)有效、實(shí)時(shí)處理監(jiān)控視頻變得尤為重要,特別是監(jiān)控監(jiān)獄犯人。為此,針對(duì)監(jiān)獄監(jiān)控區(qū)域的單一背景,本文提出了一種快速魯棒PCA方法(Fast RPCA,FaRPCA),有效學(xué)習(xí)監(jiān)控區(qū)域背景,提取前景中特定行人來(lái)達(dá)到實(shí)時(shí)監(jiān)控犯人的目的;另綜合運(yùn)用多種傳統(tǒng)技術(shù),還設(shè)計(jì)一套監(jiān)獄犯人越界檢測(cè)系統(tǒng)來(lái)識(shí)別獄警和犯人,避免報(bào)警失誤。本文具體工作如下:(1)介紹了三種經(jīng)典前景提取算法,詳細(xì)分析了高斯背景建模、RPCA及Go Dec算法的基本原理和算法優(yōu)缺點(diǎn)。(2)提出了高效的前景提取方法FaRPCA,相比RPCA和Go Dec,FaRPCA在六個(gè)基準(zhǔn)數(shù)據(jù)集上的前景檢測(cè)效率和性能更高。(3)設(shè)計(jì)了監(jiān)獄犯人越界檢測(cè)系統(tǒng)。通過(guò)集成FaRPCA前景檢測(cè)算法、Canny邊緣檢測(cè)、霍夫直線檢測(cè)、顏色識(shí)別與重心檢測(cè)方法實(shí)現(xiàn)實(shí)時(shí)監(jiān)控犯人。
[Abstract]:With the rapid development of computer intelligence technology, security system to intelligent development began to become possible. With its development and application, people do not need to focus on video with naked eyes, avoid visual fatigue caused by long working hours, so as to put an end to the occurrence of alarm errors in monitoring areas, prevent the occurrence of illegal crimes, accident cases. In addition, the security system can give full play to the computer vision technology in social public opinion monitoring, intelligent transportation and other aspects of the important role. Therefore, how to use computer vision technology effectively, real-time processing of surveillance video has become particularly important, especially for prison inmates. Therefore, aiming at the single background of prison monitoring area, a fast robust PCA method (Fast RPCA-FaRPCA) is proposed, which can effectively learn the background of the monitoring area and extract the specific pedestrian in the foreground to achieve the purpose of real-time monitoring of prisoners. In addition, a system is designed to identify prison guards and prisoners and avoid alarm errors by using a variety of traditional techniques. The main work of this paper is as follows: (1) three classical foreground extraction algorithms are introduced. The basic principles, advantages and disadvantages of Gao Si background modeling and go Dec algorithm are analyzed in detail. (2) an efficient foreground extraction method, FaRPCAA, is proposed. Compared with Gao Si and go Decn FaRPCA, the efficiency and performance of foreground detection on six datum data sets are higher. (3) The system of prison prisoner cross-border detection is designed. By integrating FaRPCA foreground detection algorithms such as Canny edge detection, Hough line detection, color recognition and center of gravity detection, real-time monitoring of prisoners is realized.
【學(xué)位授予單位】：國(guó)防科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：D916.7;TP391.41

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本文編號(hào)：2080664