本文選題：教育錄播系統(tǒng) + 人體行為識別　； 參考：《西安科技大學(xué)》2017年碩士論文

【摘要】：基于視頻圖像技術(shù)的人體行為識別算法研究已經(jīng)成為當(dāng)前研究的熱門課題,近些年取得了相當(dāng)多的研究成果,其被應(yīng)用到視頻監(jiān)控、道路交通、虛擬現(xiàn)實、醫(yī)療監(jiān)護(hù)、體育運動等領(lǐng)域。另外,隨著我國對教育事業(yè)的大力支持和高度重視,多媒體教學(xué)已經(jīng)是廣泛流行,其中對教學(xué)過程的錄制是主要的發(fā)展方向,因此基于視頻圖像跟蹤識別分析技術(shù)的智能教育錄播系統(tǒng)應(yīng)運而生。其改變了以往老師在講臺上講課,學(xué)生坐在下面聽課的教學(xué)模式,使得課后學(xué)生自己或者其他的學(xué)生可以隨時學(xué)到知識,達(dá)到教學(xué)資源的共享和再利用�；谝曨l的教學(xué)錄播系統(tǒng)主要包括教師跟蹤系統(tǒng)和學(xué)生定位系統(tǒng),本文主要是研究學(xué)生模塊的學(xué)生課堂行為識別算法,以達(dá)到教育錄播系統(tǒng)的實際需求。本文主要研究了傳統(tǒng)的人體行為識別方法,以及對教室環(huán)境下行為識別算法的分析,對學(xué)生課堂上發(fā)言或者回答問題這一過程的行為進(jìn)行識別,主要識別舉手、站立和坐下三種動作。針對研究場景的背景以及學(xué)生動作本身的特點,本文提出一種基于運動歷史圖的Zernike矩特征和樸素貝葉斯分類器分類,然后通過Lucas-Kanade光流特征和全局運動方向特征判斷運動方向的方法對動作進(jìn)行識別。在前景提取方面,研究了幾種常用的運動目標(biāo)檢測方法以及對比實驗結(jié)果,根據(jù)本文研究的應(yīng)用場景,使用背景減除法對前景進(jìn)行檢測;在特征提取方面,主要在運動歷史圖的基礎(chǔ)上提取動作的Zernike矩特征,以及提取光流特征和全局運動方向等方向特征;在分類識別方面,主要使用樸素貝葉斯分類器對學(xué)生的三種動作分類識別,將舉手動作和站立、坐下兩種動作分為兩大類;基于本文所拍攝的Student-behavior視頻庫進(jìn)行實驗測試,實驗結(jié)果表明本文所提出的方法對背景復(fù)雜的教室環(huán)境下的學(xué)生行為進(jìn)行有效識別,以及能夠?qū)δM教師和學(xué)生干擾的場景準(zhǔn)確識別,并且識別率高,具有一定的可行性和魯棒性。
[Abstract]:The research of human behavior recognition algorithm based on video image technology has become a hot topic. In recent years, a lot of research results have been obtained. It has been applied to video surveillance, road traffic, virtual reality, medical monitoring, etc. Sports, etc. In addition, with the great support and high attention paid to education in our country, multimedia teaching has become a widespread trend, in which the recording of the teaching process is the main development direction. Therefore, the intelligent education recording and broadcasting system based on video image tracking recognition and analysis technology emerges as the times require. It has changed the teaching mode in which the teacher lectured on the podium and the students sat down to listen to the class, so that the students themselves or other students can learn knowledge at any time after class, so as to achieve the sharing and reuse of teaching resources. The teaching recording and broadcasting system based on video mainly includes the teacher tracking system and the student orientation system. This paper mainly studies the students' classroom behavior identification algorithm in order to meet the actual demand of the educational recording and broadcasting system. This paper mainly studies the traditional human behavior recognition method, and the analysis of the behavior recognition algorithm in the classroom environment, the identification of the students' behavior in the process of speaking or answering questions in the classroom, mainly the identification of raising hands. Stand and sit down. According to the background of the scene and the characteristics of students' actions, this paper presents a Zernike moment feature and naive Bayesian classifier based on motion history graph. Then the motion is recognized by Lucas-Kanade optical flow feature and global motion direction feature. In the aspect of foreground extraction, several commonly used moving target detection methods and comparative experimental results are studied. According to the application scene of this paper, background subtraction method is used to detect foreground. On the basis of motion history map, Zernike moment feature of action, optical flow feature and global motion direction feature are extracted. In classification and recognition, naive Bayes classifier is mainly used to classify students' three kinds of actions. According to the Student-behavior video library taken in this paper, the experimental results show that the method proposed in this paper can effectively recognize the students' behavior in the complicated classroom environment. And it can accurately identify the scene of simulated teacher and student interference, and the recognition rate is high, which is feasible and robust.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：G434;TP391.41

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本文編號：2039825