本文選題：手勢識別 + Kinect��； 參考：《蘭州交通大學》2017年碩士論文

【摘要】：計算機科技進步的成果已經(jīng)深入到人們生活的方方面面,而聯(lián)系起人與機器之間溝通橋梁的正是人機交互技術(shù)(Human Computer Interaction,HCI)。在以用戶體驗為主導的今天,基于視覺的手勢交互以其便捷、自然和友好等優(yōu)勢,逐漸成為人機交互技術(shù)研究的重要分支。隨著Kinect等具有深度信息捕捉功能的傳感器的出現(xiàn),基于視覺的手勢識別研究有了新的發(fā)展方向。由于手勢形態(tài)在空間和時間上靈活可變,使得基于視覺的手勢識別在實際應用中有著巨大的發(fā)展?jié)摿�。本文基于Kinect傳感器就復雜背景下靜態(tài)手型和動態(tài)手指軌跡相結(jié)合的手勢識別方法開展研究,主要在手勢分割、指尖檢測、手勢特征提取、手勢識別和人機交互手勢設(shè)計方面進行了一系列的研究與實驗。本文主要工作如下:(1)手勢分割部分,針對背景中臉部和類膚色區(qū)域?qū)κ謩莘指钚Ч绊戄^大的問題,首先利用YCbCr橢圓膚色模型分割出膚色區(qū)域,并對得到圖像進行腐蝕處理以去除噪聲;其次利用Kinect傳感器所獲的深度信息對膚色區(qū)域進行投影,采用自適應深度閾值方法分割出手勢。(2)指尖定位部分,針對現(xiàn)有基于輪廓曲率定位指尖存在的問題,本文引入基于凸包的指尖定位方法,首先獲得手勢輪廓并計算其近似多邊形;其次計算輪廓凸包頂點從而獲得指尖候選點;最后通過相鄰凸缺陷最深點與凸頂點夾角和凸缺陷深度篩選出指尖,并獲取各個指尖位置信息從而得到指尖的運動特征。(3)手勢特征提取部分,為了使手勢交互更為自然并且保證識別準確率,在提取靜態(tài)手勢特征方面,本文選擇了手勢的結(jié)構(gòu)特征(指尖個數(shù),手勢輪廓周長面積比)和統(tǒng)計特征(Hu距的前四階距)組成特征向量。(4)手勢識別部分,本文構(gòu)建了支持向量機的多值分類器,對本文所設(shè)計的交互手勢的靜態(tài)手勢部分進行識別,結(jié)合手指運動特征最終識別出交互手勢并觸發(fā)操作。經(jīng)實驗分析,本文設(shè)計的手勢交互方式自然靈活且識別率高。
[Abstract]:The achievements of computer science and technology progress have penetrated into every aspect of people's life, and it is the human-computer interaction technology, Human Computer interaction, that links the bridge between human and machine. Nowadays, with the user experience as the dominant factor, visual gesture interaction has become an important branch of human-computer interaction technology for its advantages of convenience, nature and friendliness. With the appearance of sensors with depth information capture function such as Kinect, the research of hand gesture recognition based on vision has a new development direction. Because gesture forms are flexible in space and time, visual gesture recognition has great potential in practical applications. Based on the Kinect sensor, this paper studies the hand gesture recognition method based on the combination of static hand type and dynamic finger trajectory in complex background, mainly in gesture segmentation, fingertip detection, gesture feature extraction, etc. A series of researches and experiments have been carried out on gesture recognition and human-computer interaction gesture design. The main work of this paper is as follows: (1) gesture segmentation. Aiming at the problem that the facial and skin-like regions in the background have a great influence on the gesture segmentation effect, we first use the YCbCr elliptical skin color model to segment the skin color region. The image is corrupted to remove noise. Secondly, the depth information obtained by Kinect sensor is used to project the skin color area, and the finger tip location part is segmented by adaptive depth threshold method. Aiming at the problems existing in the existing fingertips location based on contour curvature, this paper introduces a fingertip localization method based on convex hull. Firstly, the gesture contour is obtained and its approximate polygon is calculated; secondly, the contour convex hull vertex is calculated to obtain the finger tip candidate points. Finally, the finger tip is screened by the angle between the deepest point of the adjacent convex defect and the convex vertex and the depth of the convex defect, and the position information of each finger tip is obtained so as to obtain the motion feature of the fingertip. In order to make gesture interaction more natural and ensure recognition accuracy, this paper chooses the structural feature of gesture (number of fingertips) to extract static gesture features. In this paper, a multi-valued classifier of support vector machine is constructed, and the static gesture part of the interactive gesture designed in this paper is recognized. Combined with finger motion features, the interactive gesture is finally recognized and the operation is triggered. Experimental analysis shows that the gesture interaction method designed in this paper is naturally flexible and has a high recognition rate.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：1933961