基于Kinect的手勢識別及其在場景驅動中的應用
發(fā)布時間：2019-02-16 08:47
【摘要】：在用戶界面研究中,人機交互技術是當前發(fā)展最迅速的技術之一,研究人員予以特別重視。它是一門綜合學科,與認知學、人機工程學、心理學等學科領域有著密切的聯系。作為人機交互中重要的一部分,手勢識別一直以來被眾多研究者重視。特別是近幾年,隨著微軟公司的Kinect的出現,符合人機交流習慣的手勢識別交互技術的研究變得非�；钴S。按照手勢動作分類,手勢識別研究包括兩部分:靜態(tài)手勢識別及動態(tài)手勢識別。本課題以微軟公司提供的Kinect為手勢動作的采集設備,對靜態(tài)手勢識別和動態(tài)手勢識別的算法分別進行優(yōu)化然后在虛擬場景中完成測試。首先,為了使手部區(qū)域分割更精確,提出一種新的手部區(qū)域分割算法。該算法通過計算軀干區(qū)域和手部區(qū)域的類間方差得到最佳分割閾值,從而提取到手部區(qū)域,再計算手部區(qū)域點密度最大的點得到掌心點,采用相應橢圓描述手掌區(qū)域的基礎上結合相應坐標系將手部區(qū)域細分成手掌區(qū)域、指尖區(qū)域和手臂區(qū)域。其次,針對靜態(tài)手勢識別過程中利用單特征識別時準確率低的問題,提出一種基于多特征提取的手勢識別算法。此算法首先提取指尖點到手掌中心點的距離、指尖點到手掌平面的距離和手掌區(qū)域三種不同的手勢特征,然后應用一個多分類的支持向量機(SVM)分類器對靜態(tài)手勢進行分類,并在手勢數據庫中完成了算法驗證。第三,針對動態(tài)手勢識別過程中關節(jié)點獲取不準確的問題,提出一種利用關節(jié)點可信度度量關節(jié)點有效性的算法。此算法通過計算關節(jié)點的行為可信度、運動學可信度和彩色圖像可信度及其可信度的特征權重,可更準確獲取動態(tài)手勢的關節(jié)點,從而完成快速準確的動態(tài)手勢識別。最后,在基于3ds Max和Unity 3d設計的三維虛擬場景中完成實時檢測。結合靜態(tài)手勢和動態(tài)手勢識別技術,設計包括開始、指向、轉向、放縮、揮手及停止等手勢動作,驅動虛擬場景完成相應功能的實時變化,驗證了算法的有效性。
[Abstract]:In the research of user interface, human-computer interaction is one of the most rapidly developing technologies, and researchers pay special attention to it. It is a comprehensive subject and has close relation with cognitive science, ergonomics, psychology and so on. As an important part of human-computer interaction, gesture recognition has been paid attention to by many researchers. Especially in recent years, with the emergence of Microsoft Kinect, the research on gesture recognition and interaction technology, which accords with man-machine communication habits, has become very active. According to gesture classification, gesture recognition includes two parts: static gesture recognition and dynamic gesture recognition. In this paper, the Kinect provided by Microsoft is used as the acquisition device of gesture action. The algorithms of static gesture recognition and dynamic gesture recognition are optimized and tested in virtual scene. Firstly, in order to make hand region segmentation more accurate, a new hand region segmentation algorithm is proposed. The algorithm obtains the optimal segmentation threshold by calculating the variance between the torso region and the hand region, and then extracts the hand region, and then calculates the point with the highest density in the hand region to get the centerpoint. On the basis of describing the palm region with the corresponding ellipse, the hand region is subdivided into palm region, fingertip region and arm region in the corresponding coordinate system. Secondly, aiming at the problem of low accuracy when using single feature in static gesture recognition, a gesture recognition algorithm based on multi-feature extraction is proposed. The algorithm firstly extracts the distance from the fingertip to the center of the palm, the distance from the fingertip to the palm plane and three different gesture features in the palm area. Then, a multi-classification support vector machine (SVM) classifier is used to classify the static gestures. The algorithm is verified in the gesture database. Thirdly, aiming at the problem of inaccuracy of node acquisition in dynamic gesture recognition, an algorithm is proposed to measure the effectiveness of the node by using the reliability of the node. By calculating the behavioral credibility, kinematics credibility and the feature weights of the color image credibility, the algorithm can obtain the dynamic gesture nodes more accurately, so as to complete the fast and accurate dynamic gesture recognition. Finally, real-time detection is completed in a three-dimensional virtual scene based on 3ds Max and Unity 3D design. Combined with static gesture and dynamic gesture recognition technology, the design includes start, point, turn, drop, wave and stop gestures, drive the virtual scene to complete the corresponding real-time changes, and verify the effectiveness of the algorithm.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
本文編號：2424269
[Abstract]:In the research of user interface, human-computer interaction is one of the most rapidly developing technologies, and researchers pay special attention to it. It is a comprehensive subject and has close relation with cognitive science, ergonomics, psychology and so on. As an important part of human-computer interaction, gesture recognition has been paid attention to by many researchers. Especially in recent years, with the emergence of Microsoft Kinect, the research on gesture recognition and interaction technology, which accords with man-machine communication habits, has become very active. According to gesture classification, gesture recognition includes two parts: static gesture recognition and dynamic gesture recognition. In this paper, the Kinect provided by Microsoft is used as the acquisition device of gesture action. The algorithms of static gesture recognition and dynamic gesture recognition are optimized and tested in virtual scene. Firstly, in order to make hand region segmentation more accurate, a new hand region segmentation algorithm is proposed. The algorithm obtains the optimal segmentation threshold by calculating the variance between the torso region and the hand region, and then extracts the hand region, and then calculates the point with the highest density in the hand region to get the centerpoint. On the basis of describing the palm region with the corresponding ellipse, the hand region is subdivided into palm region, fingertip region and arm region in the corresponding coordinate system. Secondly, aiming at the problem of low accuracy when using single feature in static gesture recognition, a gesture recognition algorithm based on multi-feature extraction is proposed. The algorithm firstly extracts the distance from the fingertip to the center of the palm, the distance from the fingertip to the palm plane and three different gesture features in the palm area. Then, a multi-classification support vector machine (SVM) classifier is used to classify the static gestures. The algorithm is verified in the gesture database. Thirdly, aiming at the problem of inaccuracy of node acquisition in dynamic gesture recognition, an algorithm is proposed to measure the effectiveness of the node by using the reliability of the node. By calculating the behavioral credibility, kinematics credibility and the feature weights of the color image credibility, the algorithm can obtain the dynamic gesture nodes more accurately, so as to complete the fast and accurate dynamic gesture recognition. Finally, real-time detection is completed in a three-dimensional virtual scene based on 3ds Max and Unity 3D design. Combined with static gesture and dynamic gesture recognition technology, the design includes start, point, turn, drop, wave and stop gestures, drive the virtual scene to complete the corresponding real-time changes, and verify the effectiveness of the algorithm.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
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