發(fā)布時間：2018-03-09 15:47

  本文選題：人機(jī)交互　切入點(diǎn)：Kinect　出處：《安徽大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著計(jì)算機(jī)、信息化的發(fā)展,人機(jī)交互在辦公以及生活中顯得越來越重要,自然用戶界面已經(jīng)成為人機(jī)交互發(fā)展的趨勢,目前研究比較熱門的有用語言、動作及表情等與計(jì)算機(jī)進(jìn)行交互。手勢識別作為自然人機(jī)交互的一項(xiàng)關(guān)鍵的技術(shù),具有靈活、直觀、簡單等優(yōu)點(diǎn),成為動作與計(jì)算機(jī)交互研究領(lǐng)域中的熱點(diǎn),在業(yè)內(nèi)引起了很多學(xué)者的關(guān)注和研究。手勢識別的發(fā)展由早起的佩戴數(shù)據(jù)手套逐漸的轉(zhuǎn)向基于視覺方法的研究,目前大多的手勢識別研究是在計(jì)算機(jī)視覺的基礎(chǔ)上進(jìn)行的,通過攝像頭直接獲取背景空間的信息,無需佩戴復(fù)雜、昂貴的輸入設(shè)備。眾所周知的觸摸屏技術(shù)現(xiàn)已發(fā)展的比較成熟,但大多應(yīng)用于小屏幕的操作上,例如:手機(jī)、筆記本、小型觸摸屏等。針對大屏幕的操作,目前缺乏較有效的交互手段。本文對動態(tài)手勢識別進(jìn)行了研究并設(shè)計(jì)了一套動態(tài)手勢識別交互系統(tǒng),實(shí)現(xiàn)了人與大屏幕的實(shí)時互動操作。Kinect是微軟2010年研發(fā)出來的,能夠在復(fù)雜的背景環(huán)境下快速準(zhǔn)確的獲取人體骨骼節(jié)點(diǎn)信息。本文以Kinect為輸入設(shè)備,獲取人體手部、手腕、肘部和脊椎這幾個關(guān)鍵的骨骼節(jié)點(diǎn)信息作為實(shí)驗(yàn)的特征向量。主要工作有以下幾個方面:針對手勢識別的研究,分析了整個識別過程,對識別過程中每個階段采用的技術(shù)做了詳細(xì)的調(diào)查和研究。闡述了 Kinect設(shè)備在獲取深度信息上的強(qiáng)大功能以及基于Kienct手勢識別研究的現(xiàn)狀和優(yōu)點(diǎn)。研究了動態(tài)手勢的識別過程,針對手勢識別起點(diǎn)和終點(diǎn)的檢測,本文提出一種基于特殊手勢判斷的方法。定義一種靜態(tài)手勢,通過識別定義的手勢的判斷動態(tài)手勢是否開始,對于動態(tài)手勢的結(jié)束則根據(jù)計(jì)算連續(xù)多幀內(nèi)特征節(jié)點(diǎn)運(yùn)動的距離判斷。在動態(tài)手勢識別過程中,針對手勢識別的效率和速率問題,對傳統(tǒng)的動態(tài)時間規(guī)整(Dynamic Time Warping,DTW)算法進(jìn)行了改進(jìn)。首先,利用特征節(jié)點(diǎn)在運(yùn)動過程中的方差對其進(jìn)行了權(quán)值的動態(tài)分配,以便提高識別效率。其次,對DTW算法的搜索路徑進(jìn)行了詳細(xì)的分析,采取點(diǎn)與線結(jié)合的方法對其搜索路徑進(jìn)行約束,減少計(jì)算量,提高識別速率。最后,將改進(jìn)后的DTW算法與K近鄰(K-nearest neighbor,KNN)算法進(jìn)行結(jié)合用于識別手勢,能夠有效的提高識別效率。最后,設(shè)計(jì)并實(shí)現(xiàn)了一個與手勢識別相關(guān)的大屏幕互動系統(tǒng),在系統(tǒng)中手勢代替了鼠標(biāo)的部分功能,直接通過不同的手勢對大屏幕上的內(nèi)容進(jìn)行操作,整個系統(tǒng)運(yùn)行流暢,讓人真實(shí)的體會到人機(jī)交互。
[Abstract]:With the development of computer and information, human-computer interaction is becoming more and more important in office and daily life. The natural user interface has become the trend of human-computer interaction. Gesture recognition, as a key technology of natural human-computer interaction, has the advantages of flexibility, intuition and simplicity, and has become a hot spot in the field of interaction between action and computer. The development of gesture recognition has gradually shifted from the early wearing of data gloves to the research based on visual methods. At present, most of the research on gesture recognition is carried out on the basis of computer vision. Access to background information directly through the camera without the need to wear complex, expensive input devices. The well-known touch screen technology is now relatively mature, but it is mostly used in small screen operations, such as mobile phones, laptops, etc. Small touch screen and so on. For the operation of large screen, there is a lack of effective interactive means. In this paper, dynamic gesture recognition is studied and a set of dynamic gesture recognition interaction system is designed. The real-time interactive operation of human and large screen. Kinect is developed by Microsoft in 2010. It can quickly and accurately obtain human skeleton node information in complex background environment. In this paper, Kinect is used as input device to obtain human hands and wrists. Elbows and vertebrae are the key information of bone nodes as the feature vectors of the experiment. The main work includes the following aspects: according to the research of gesture recognition, the whole recognition process is analyzed. This paper makes a detailed investigation and research on the technology used in each stage of the recognition process, expounds the powerful function of Kinect equipment in obtaining depth information and the present situation and advantages of the research on gesture recognition based on Kienct, and studies the process of dynamic gesture recognition. Aiming at the detection of the beginning and end of gesture recognition, this paper proposes a method based on special gesture judgment, which defines a static gesture and determines whether the dynamic gesture begins or not by recognizing the defined gesture. For the end of dynamic gesture, the distance of motion of feature nodes in successive frames is calculated. In the process of dynamic gesture recognition, the efficiency and rate of gesture recognition are discussed. The traditional dynamic Time warping (DTW) algorithm is improved. Firstly, the weights are dynamically assigned by the variance of feature nodes in the process of motion in order to improve the recognition efficiency. The search path of DTW algorithm is analyzed in detail, and the search path is constrained by the combination of point and line, which can reduce the amount of calculation and improve the recognition rate. The improved DTW algorithm and K-nearest neighbor KNN algorithm are combined to recognize gestures, which can effectively improve the recognition efficiency. Finally, a large screen interactive system related to gesture recognition is designed and implemented. In the system hand gestures replace some of the functions of the mouse directly through different gestures on the large screen content to operate the whole system runs smoothly let people truly understand the human-computer interaction.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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