本文選題：智能視頻監(jiān)控　切入點：哈爾型特性局部二元模式　出處：《中國科學技術(shù)大學》2017年碩士論文

【摘要】：當今科技發(fā)展日新月異,社會經(jīng)濟水平穩(wěn)步提升,人口的流動性日益增大,大量的流動人口給社會治安帶來挑戰(zhàn),傳統(tǒng)的安防監(jiān)控系統(tǒng)依靠人力對場景視頻進行分析處理,沒有充分利用計算機技術(shù)以最大化視頻監(jiān)控的價值。近年來,計算機視覺、機器學習等技術(shù)不斷發(fā)展,智能視頻監(jiān)控系統(tǒng)日趨成熟,在眾多公共場所中得到了大量應用,相比于傳統(tǒng)方法,它不僅能減輕監(jiān)控人員的工作負擔、節(jié)約成本,還能提高數(shù)據(jù)處理、異常檢測的能力。結(jié)合當前先進的理論和技術(shù),智能視頻監(jiān)控系統(tǒng)將在建設(shè)智慧城市、平安城市等方面發(fā)揮重要作用。由于監(jiān)控場景的多樣性和復雜性,目前智能視頻監(jiān)控還面臨許多問題和挑戰(zhàn),本文主要針對運動陰影檢測以及運動目標識別問題進行研究,主要工作如下:(1)分析了產(chǎn)生運動陰影的基本原理及其相關(guān)性質(zhì),介紹了不同類型的運動陰影檢測算法。分析了經(jīng)典的運動目標檢測算法并對基于碼本的方法進行了詳細說明,對目標分類中常用的特征及分類器進行了介紹。(2)提出了一種基于哈爾型特性局部二元模式(Haar Local Binary Pattern,HLBP)特征的運動陰影檢測算法。該算法分別提取運動區(qū)域及其背景區(qū)域的HLBP特征向量,不需要進行閾值選取、圖像分塊以及直方圖統(tǒng)計,使用曼哈頓距離度量紋理差異,對差異圖像進行閾值分割以檢測運動陰影。結(jié)合顏色空間信息及紋理信息,提出了一種基于隨機森林的運動陰影檢測方法,該方法不需要對應用場景的光照、反射性質(zhì)等進行假設(shè),也避免了參數(shù)的設(shè)置。使用隨機森林分類器對各個像素點進行二分類,判決是陰影還是前景目標像素點。該方法對各種室內(nèi)外場景能取得較好的效果并且具有一定的泛化能力。(3)提出了一種基于HOG-HLBP特征的運動目標識別方法。利用方向梯度直方圖(Histogram of Oriented Gradient,HOG)對目標外形輪廓以及HLBP對紋理的描述能力,將兩者結(jié)合形成HOG-HLBP特征,使用支持向量機(Support Vector Machines,SVM)進行多類別判決,實驗證明該方法能取得較好的分類效果。實現(xiàn)了一種基于檢測的多目標跟蹤方法,在匹配代價中加入表觀信息,使其能夠應對一些遮擋情況。結(jié)合運動檢測、運動陰影消除以及多目標跟蹤實現(xiàn)了監(jiān)控視頻目標分類。
[Abstract]:With the rapid development of science and technology, the steady improvement of social economic level, the increasing mobility of the population, a large number of floating population bring challenges to social security. The traditional security monitoring system relies on manpower to analyze and process the scene video.Computer technology is not fully utilized to maximize the value of video surveillance.In recent years, with the continuous development of computer vision, machine learning and other technologies, intelligent video surveillance system is becoming more and more mature, and has been widely used in many public places. Compared with traditional methods, it can not only reduce the workload of supervisors.Save cost and improve the ability of data processing and anomaly detection.Based on the advanced theory and technology, intelligent video surveillance system will play an important role in the construction of intelligent city and peaceful city.Because of the diversity and complexity of surveillance scene, intelligent video surveillance still faces many problems and challenges. This paper mainly focuses on moving shadow detection and moving target recognition.The main work is as follows: (1) the basic principle of motion shadow generation and its related properties are analyzed, and different kinds of motion shadow detection algorithms are introduced.The classical moving target detection algorithm is analyzed and the codebook based method is described in detail.This paper introduces the features and classifiers commonly used in target classification, and presents a motion shadow detection algorithm based on Haar Local Binary pattern feature of Hal type.The algorithm extracts the HLBP feature vectors of the moving region and the background region, and does not need to select the threshold, divide the image and statistics the histogram, and measure the texture difference using the Manhattan distance.The difference image is segmented by threshold to detect the moving shadow.Combined with color space information and texture information, a moving shadow detection method based on random forest is proposed. This method does not need to hypothesize the illumination and reflection properties of the application scene, and also avoids the setting of parameters.A random forest classifier is used to classify each pixel to determine whether it is a shadow or a foreground pixel.This method can achieve good results for various indoor and outdoor scenes and has a certain generalization ability. A moving target recognition method based on HOG-HLBP features is proposed.Using histogram of Oriented gradient histogram (histogram) to describe the contour of the target and the ability of HLBP to describe the texture, the two methods are combined to form the HOG-HLBP feature, and the support vector machine (SVM) is used to make the multi-class decision.Experiments show that this method can achieve better classification effect.A multi-target tracking method based on detection is implemented. The apparent information is added to the matching cost to enable it to deal with some occlusion cases.Combined with motion detection, motion shadow cancellation and multi-target tracking, video target classification is realized.
【學位授予單位】：中國科學技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：1722644