本文選題：虛擬煤礦火災(zāi) + Kinect人機(jī)交互��； 參考：《山東科技大學(xué)》2017年碩士論文

【摘要】：隨著虛擬現(xiàn)實(shí)和Kinect人機(jī)交互技術(shù)的發(fā)展,Kinect越來越多的被應(yīng)用到體感游戲及大型場景展示上,用來與虛擬場景進(jìn)行互動(dòng)。本文主要研究了基于Kinect的人機(jī)交互技術(shù)及其在虛擬煤礦巷道火災(zāi)逃生模擬系統(tǒng)中的應(yīng)用。首先研究了整個(gè)系統(tǒng)的軟硬件配置,其次研究了如何利用Kinect捕捉人體深度圖像以及骨骼數(shù)據(jù);然后研究了基于深度圖像的正反手勢識(shí)別,以及基于骨骼數(shù)據(jù)的體態(tài)識(shí)別;最后研究了利用UE4創(chuàng)建虛擬巷道模型,用正反手勢控制巷道中人物的前后左右行走以及利用步態(tài)識(shí)別算法識(shí)別操作者的身份,并將該操作者的信息在虛擬巷道中顯示出來。在進(jìn)行正反手勢識(shí)別時(shí),首先利用Kinect for Window SDK 2.0的庫函數(shù)讀取深度信息,再利用閾值分割將手部區(qū)域分割出來,然后將手部圖像分為兩層:第一層為直立手指層,第二層為蜷縮手指層。對(duì)于直立手指層,先利用輪廓檢測算法檢測出手部輪廓,再利用凸包檢測算法識(shí)別出手指的個(gè)數(shù);對(duì)于蜷縮手指層,主要利用輪廓檢測算法檢測輪廓,有輪廓?jiǎng)t為正手,無輪廓?jiǎng)t為負(fù)手。最后把這兩層識(shí)別結(jié)果結(jié)合起來,實(shí)現(xiàn)正反手識(shí)別。步態(tài)識(shí)別也是先通過Kinect體感設(shè)備及Kinect for Windows SDK 2.0讀取骨骼數(shù)據(jù)。進(jìn)行識(shí)別之前,首先分析人走路的姿態(tài),提取特征向量。人走路時(shí)主要特征有速度,步長,各個(gè)肢體擺動(dòng)的幅度,本文中主要選擇10個(gè)肢體角度進(jìn)行分析。然后,畫出每個(gè)肢體角度隨時(shí)間變化的曲線,從中提取一個(gè)平均周期,并用多項(xiàng)式函數(shù)擬合。最后,用多項(xiàng)式的擬合參數(shù)作為特征,用KNN分類算法,與數(shù)據(jù)庫中的參數(shù)進(jìn)行匹配,識(shí)別出人的身份。
[Abstract]:With the development of virtual reality and Kinect human-computer interaction technology Kinect is more and more used in body sense games and large-scale scene display to interact with virtual scene. This paper mainly studies the human-computer interaction technology based on Kinect and its application in virtual mine tunnel fire escape simulation system. Firstly, the hardware and software configuration of the whole system is studied, secondly, how to capture the human depth image and bone data by Kinect, then the forward and negative gesture recognition based on the depth image and the posture recognition based on the bone data are studied. Finally, the virtual laneway model is created by using UE4, the forward and left walking of the characters in the roadway is controlled by positive and negative gestures and the identity of the operator is recognized by gait recognition algorithm, and the information of the operator is displayed in the virtual laneway. When using the library function of Kinect for window SDK 2.0 to read the depth information, the hand region is segmented by threshold segmentation, and then the hand image is divided into two layers: the first layer is the vertical finger layer. The second layer is the crouching finger layer. For the vertical finger layer, the contour detection algorithm is first used to detect the contour of the hand, and then the number of fingers is recognized by using the convex hull detection algorithm; for the curled finger layer, the contour detection algorithm is mainly used to detect the contour, while the contour is forehand. No contours are negative hands. Finally, the recognition results of the two layers are combined to realize forward and backhand recognition. Gait recognition also uses Kinect somatosensory devices and Kinect for SDK 2.0 to read bone data. Before recognition, the attitude of human walking is analyzed, and the feature vector is extracted. The main characteristics of human walking are speed, step size and swing amplitude of each limb. In this paper, 10 limb angles are selected for analysis. Then, the curve of each limb angle changing with time is drawn, from which an average period is extracted and fitted with polynomial function. Finally, the fitting parameters of the polynomial are used as the feature, and the KNN classification algorithm is used to match the parameters in the database to identify the identity of the person.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TD752;TP391.41

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