本文選題：嵌入式系統(tǒng)　切入點(diǎn)：AdaBoost　出處：《青島理工大學(xué)》2010年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著商業(yè)的發(fā)展,商家對(duì)投放廣告所產(chǎn)生的效果越來(lái)越重視,廣告效果評(píng)估與智能播放是兩個(gè)重要的研究?jī)?nèi)容。本文設(shè)計(jì)了一套智能廣告播放與效果評(píng)估系統(tǒng),通過采集廣告受眾的人臉圖像,對(duì)人臉進(jìn)行檢測(cè)、跟蹤與識(shí)別,判斷其所屬的消費(fèi)群體,進(jìn)而播放相應(yīng)的廣告并統(tǒng)計(jì)效果評(píng)估參數(shù)。 本系統(tǒng)的目標(biāo)是設(shè)計(jì)一個(gè)嵌入式處理終端,自動(dòng)實(shí)現(xiàn)以上功能。硬件平臺(tái)采用ADI公司生產(chǎn)的Blackfin系列DSP,算法主要包括人臉檢測(cè)、跟蹤和人臉分類,然后根據(jù)處理結(jié)果進(jìn)行統(tǒng)計(jì)分析和智能播放,相關(guān)算法在PC機(jī)上進(jìn)行了開發(fā)和調(diào)試,最后給出了向DSP進(jìn)行移植的方法。 在前人研究的基礎(chǔ)上,本文主要針對(duì)人臉檢測(cè)、人臉跟蹤和人臉識(shí)別三個(gè)問題進(jìn)行了研究: 1、在AdaBoost人臉檢測(cè)算法的基礎(chǔ)上,融合了膚色檢驗(yàn)算法以降低誤檢率。AdaBoost算法主要利用人臉灰度圖像信息,在檢測(cè)過程中易將非人臉區(qū)域誤識(shí)別為人臉區(qū)域,采用膚色匹配策略,將不在人體膚色范圍內(nèi)的檢測(cè)結(jié)果去除,降低了誤檢率。 2、在粒子濾波理論框架下,討論了基于粒子濾波的目標(biāo)跟蹤算法。從目標(biāo)運(yùn)動(dòng)模型、目標(biāo)觀測(cè)模型、粒子重采樣等幾個(gè)方面分別介紹了跟蹤算法的具體實(shí)現(xiàn)方法。并且,針對(duì)粒子濾波算法的復(fù)雜性,提出了結(jié)合Mean Shift算法的粒子采樣策略,取得了良好的跟蹤效果。 3、采用支持向量機(jī)對(duì)人臉進(jìn)行分類�；赟VM原理,利用LIBSVM庫(kù),通過前期參數(shù)設(shè)計(jì)及正反樣本訓(xùn)練,將人臉分為男女兩類,取得了較好的分類效果。
[Abstract]:With the development of business, merchants pay more and more attention to the effect of advertising. The evaluation of advertising effect and intelligent play are two important research contents. This paper designs a set of intelligent advertising play and effect evaluation system. By collecting the face images of the advertising audience, this paper detects, tracks and recognizes the faces, judges the consumer groups to which they belong, and then plays the corresponding advertisements and calculates the evaluation parameters of the effects. The goal of this system is to design an embedded processing terminal, which can realize the above functions automatically. The hardware platform adopts Blackfin series DSP produced by ADI Company. The algorithm mainly includes face detection, tracking and face classification. Then the statistical analysis and intelligent playback are carried out according to the processing results. The related algorithms are developed and debugged on the PC. Finally, the method of porting to DSP is given. On the basis of previous studies, this paper mainly focuses on three problems: face detection, face tracking and face recognition. 1. Based on the AdaBoost face detection algorithm, the skin color detection algorithm is fused to reduce the false detection rate. AdaBoost algorithm mainly uses the face gray image information. In the process of detection, the non-face region is easily recognized as the face region, and the skin color matching strategy is adopted. The detection results which are not within the range of human skin color are removed and the false detection rate is reduced. 2. Under the framework of particle filter theory, this paper discusses the target tracking algorithm based on particle filter, and introduces the specific implementation methods of the tracking algorithm from several aspects, such as target motion model, target observation model, particle resampling and so on. Aiming at the complexity of particle filter algorithm, a particle sampling strategy combined with Mean Shift algorithm is proposed, and a good tracking effect is obtained. 3. Based on the principle of SVM, the face is classified by support vector machine (SVM). The face is classified into male and female by using LIBSVM library and the design of pre-parameters and the training of positive and negative samples, and the classification effect is good.
【學(xué)位授予單位】：青島理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2010
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

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本文編號(hào)：1587477