本文選題：改進(jìn)的高斯背景模型 + 陰影檢測��； 參考：《華中科技大學(xué)》2014年碩士論文

【摘要】：進(jìn)入21世紀(jì)，城市公共安全已經(jīng)越來越成為人們所關(guān)注的問題。這一切，都迫切要求發(fā)展城市安防監(jiān)控系統(tǒng)。傳統(tǒng)的城市安防監(jiān)控系統(tǒng)采用的是人工監(jiān)控的方式，這種監(jiān)控方式隨著城市監(jiān)控攝像頭的個數(shù)的激增存在著工作量過大的問題。因而，安防監(jiān)控系統(tǒng)的智能化迫在眉睫，本文的目標(biāo)即是實現(xiàn)了一個這樣智能的、無人工干預(yù)的、基于視頻的城市安防監(jiān)控系統(tǒng)。系統(tǒng)主要實現(xiàn)的功能包括實時在線的異常監(jiān)控與事后取證時智能視頻檢索。系統(tǒng)的實現(xiàn)方案主要包括運動目標(biāo)檢測、運動目標(biāo)跟蹤、異常檢測三個核心模塊。在運動目標(biāo)檢測模塊，，本文采用的方法是改進(jìn)的混合高斯背景建模。針對傳統(tǒng)的混合高斯背景建模存在的第一幀初始化不全是背景會出現(xiàn)“鬼影”、原始方差的更新策略會導(dǎo)致噪聲過多、算法計算存在冗余這三個問題，本文分別相應(yīng)提出了更新率自適應(yīng)、方差設(shè)定閾值以及K值自適應(yīng)的改進(jìn)策略。對于運動檢測中陰影、光線的矯正以及后處理模塊，本文也都相應(yīng)提出了解決方案。在運動目標(biāo)跟蹤模塊，本文基于經(jīng)典的團(tuán)跟蹤進(jìn)行改進(jìn)，針對經(jīng)典團(tuán)跟蹤算法中團(tuán)的對應(yīng)方式中缺失的團(tuán)的多對多對應(yīng)關(guān)系情形本文予以補充，而針對團(tuán)跟蹤在靜態(tài)遮擋和動態(tài)遮擋情形下失效的情況，本文提出了相應(yīng)的線性預(yù)測和分塊跟蹤的解決方案。在異常檢測模塊，本文通過事先的異常定義結(jié)合目標(biāo)的位置、軌跡與運動狀態(tài)從而能夠?qū)崟r在線的檢測出異常。對于事先不能夠預(yù)測的異常，本文提出予以事后視頻檢索的方式進(jìn)行補充。最后，通過系統(tǒng)測試與功能測試模塊證明，本文提出的智能視頻監(jiān)控系統(tǒng)能夠?qū)崿F(xiàn)預(yù)先設(shè)定的功能，并且有較好的實時性與優(yōu)越的算法性能。
[Abstract]:In the twenty-first Century, urban public security has become more and more concern. All of these are urgently required to develop the urban security monitoring system. The traditional urban security monitoring system is used by artificial monitoring. With the increase of the number of urban surveillance cameras, there is a large amount of work. Therefore, the intelligence of security monitoring and control system is imminent. The aim of this paper is to realize an intelligent, non intervention, video based urban security monitoring system. The main functions of the system include real-time online anomaly monitoring and intelligent video retrieval when taking evidence afterwards. The realization scheme of the system mainly includes movement. Target detection, moving target tracking, anomaly detection three core modules. In the moving target detection module, the method used in this paper is an improved hybrid Gauss background modeling. For the traditional mixed Gauss background modeling, the first frame initialization is not complete in the background, the background will appear "ghost shadow", the original variance updating strategy will lead to the noise. There are three problems of redundancy in algorithm computing. In this paper, the adaptive updating rate adaptive, variance setting threshold and K value adaptive strategy are put forward respectively. In motion detection, the shadow, light correction and post-processing module are also proposed. In the motion target tracking module, this paper is based on the classic. The regiment tracking is improved to supplement the multiple to multi corresponding relationship of the missing groups in the regiment's correspondence method in the classical regiment tracking algorithm, and for the case of the failure of the regiment tracking in the static occlusion and dynamic occlusion, the corresponding linear prediction and block tracking solution is proposed in this paper. In this paper, the abnormity can be detected in real time by combining with the location of the target, the trajectory and the state of motion in advance. For the exception that can not be predicted in advance, this paper puts forward a method to supplement the video retrieval after the event. Finally, it is proved by the system test and function test module that the intelligent video surveillance proposed in this paper is proposed. The system can achieve pre set function, and has better real-time performance and superior algorithm performance.

【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN948.6

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本文編號：1833230