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

【摘要】：隨著商業(yè)的發(fā)展,商家對投放廣告所產生的效果越來越重視,廣告效果評估與智能播放是兩個重要的研究內容。本文設計了一套智能廣告播放與效果評估系統(tǒng),通過采集廣告受眾的人臉圖像,對人臉進行檢測、跟蹤與識別,判斷其所屬的消費群體,進而播放相應的廣告并統(tǒng)計效果評估參數(shù)。 本系統(tǒng)的目標是設計一個嵌入式處理終端,自動實現(xiàn)以上功能。硬件平臺采用ADI公司生產的Blackfin系列DSP,算法主要包括人臉檢測、跟蹤和人臉分類,然后根據(jù)處理結果進行統(tǒng)計分析和智能播放,相關算法在PC機上進行了開發(fā)和調試,最后給出了向DSP進行移植的方法。 在前人研究的基礎上,本文主要針對人臉檢測、人臉跟蹤和人臉識別三個問題進行了研究: 1、在AdaBoost人臉檢測算法的基礎上,融合了膚色檢驗算法以降低誤檢率。AdaBoost算法主要利用人臉灰度圖像信息,在檢測過程中易將非人臉區(qū)域誤識別為人臉區(qū)域,采用膚色匹配策略,將不在人體膚色范圍內的檢測結果去除,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跟蹤算法。從目標運動模型、目標觀測模型、粒子重采樣等幾個方面分別介紹了跟蹤算法的具體實現(xiàn)方法。并且,針對粒子濾波算法的復雜性,提出了結合Mean Shift算法的粒子采樣策略,取得了良好的跟蹤效果。 3、采用支持向量機對人臉進行分類�；赟VM原理,利用LIBSVM庫,通過前期參數(shù)設計及正反樣本訓練,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.
【學位授予單位】：青島理工大學
【學位級別】：碩士
【學位授予年份】：2010
【分類號】：TP391.41

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相關期刊論文 前1條

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本文編號：1587477