基于KNN算法的空間手勢識別研究與應用
發(fā)布時間：2018-11-07 08:49
【摘要】：近些年互聯(lián)網(wǎng)和計算機硬件發(fā)展迅速,為互聯(lián)網(wǎng)其它領域的發(fā)展打下了堅實的基礎,在理論研究方面機器學習和人工智能均是研究的熱點,應用方面VR和AR等虛擬現(xiàn)實設備也進一步成熟起來,很多公司已經(jīng)推出了面向市場的產(chǎn)品線,雖然在人工智能方面還不能做到容錯率媲美人類,但是現(xiàn)如今我們的生活已經(jīng)被不少人工智能的產(chǎn)品所包圍比如:手機中的手寫識別與微信中的語音識別等應用場景。這些智能場景的應用是依托于計算機分類學習算法的大力發(fā)展,基礎的分類方法如KNN和SVM分類,經(jīng)過多年的研究和應用已經(jīng)取得了滿意的分類精度和快速的分類速度,KNN在數(shù)據(jù)量少的場景下是一個經(jīng)典的分類解決方案,同時SVM也廣泛的應用在二分類問題領域互相之間有一定的互補性。如上所示,人工智能的發(fā)展給用戶帶來了便捷的操作的同時極大的提升了用戶體驗,并且在其他領域也有著非常大的發(fā)展?jié)摿ΑR和AR等虛擬現(xiàn)實設備以及應用也在過去的兩年中獲得廣泛關注和快速的發(fā)展,硬件的升級已經(jīng)可以支撐其讓虛擬現(xiàn)實走進我們的生活,HTC的虛擬頭盔VIVE以及微軟的AR眼鏡Hololens從發(fā)布以來就吸引了無數(shù)的目光。本文通過對國內(nèi)外研究現(xiàn)狀進行分析以及對當下虛擬現(xiàn)實領域中的各種應用實踐,在深入研究機器學習分類算法和HTC的VIVE設備應用系統(tǒng)的基礎之上,提出基于KNN算法的空間手勢識別研究并且在VR環(huán)境下進行了實際應用。研究內(nèi)容包括:(1)探究如何解決VR中的人機交互需求,如何縮小用戶和設備之間溝通的距離,讓用戶更加自然的沉浸到虛擬現(xiàn)實設備所構建的場景中去,在HTC的VIVE設備中通過一個類似于鼠標的控制器來讓用戶控制其實現(xiàn)操作,但是這種交互操作時常會干擾到用戶的正常使用,體驗并不完善,探究在此硬件設備的基礎上使用機器學習分類算法搭建手勢識別場景。(2)深入研究KNN和SVM分類算法原理和運行機制,對其基本的應用思想和代碼結構進行深入理解,在此基礎上分析不同分類算法的優(yōu)缺點,并根據(jù)所要構建的VR手勢識別應用,分析其適用度,做出算法的選擇,并從算法實現(xiàn)復雜度和算法性能兩個方向來嘗試優(yōu)化。(3)對KNN和SVM算法進行編程實現(xiàn),使用標準數(shù)據(jù)集測試其效率和性能,通過對相關參數(shù)的調優(yōu),如KNN算法中K值的選取和SVM算法中的相關參數(shù)的調試來實現(xiàn)算法優(yōu)化。最終通過對KNN和SVM進行組合應用,取長補短,實現(xiàn)分類效果的優(yōu)化,并得出優(yōu)化分析報告。(4)掌握當下VR前沿技術,在HTC的VIVE平臺中利用Unity游戲開發(fā)引擎構建手勢識別應用場景以及使用Python語言實現(xiàn)相關服務器數(shù)據(jù)傳輸和回傳結果的功能模塊,最終實現(xiàn)的手勢識別應用。
[Abstract]:In recent years, the rapid development of Internet and computer hardware has laid a solid foundation for the development of other areas of the Internet. Machine learning and artificial intelligence are hot spots in theoretical research. Virtual reality devices such as VR and AR have matured further in applications, and many companies have launched market-oriented product lines, although in artificial intelligence fault tolerance is not comparable to that of humans. But now our lives are surrounded by artificial intelligence products such as handwritten recognition in mobile phones and voice recognition in WeChat. The application of these intelligent scenes depends on the development of computer classification learning algorithms. The basic classification methods, such as KNN and SVM classification, have achieved satisfactory classification accuracy and fast classification speed after many years of research and application. KNN is a classical classification solution in the case of small amount of data, and SVM is also widely used in the field of two classification problems, which is complementary to each other to some extent. As shown above, the development of artificial intelligence not only brings convenience to users, but also greatly improves the user experience. Virtual reality devices and applications, such as VR and AR, have also received extensive attention and rapid development in the past two years. Hardware upgrades have enabled virtual reality to come into our lives, with HTC's virtual helmet VIVE and Microsoft's AR Glass Hololens attracting a lot of attention since its launch. Based on the analysis of domestic and international research status and various application practices in the field of virtual reality, this paper deeply studies the machine learning classification algorithm and the VIVE device application system of HTC. Spatial gesture recognition based on KNN algorithm is proposed and applied in VR environment. The research contents include: (1) explore how to solve the need of human-computer interaction in VR, how to narrow the communication distance between users and devices, so that users can immerse themselves more naturally in the scene constructed by virtual reality devices. In HTC's VIVE device, a mouse-like controller is used to allow the user to control its implementation, but this interaction often interferes with the user's normal use and the experience is not perfect. On the basis of this hardware device, this paper uses machine learning classification algorithm to build gesture recognition scene. (2) deeply study the principle and running mechanism of KNN and SVM classification algorithm, and deeply understand its basic application thought and code structure. On this basis, the advantages and disadvantages of different classification algorithms are analyzed, and according to the VR gesture recognition application to be constructed, the applicability of the algorithm is analyzed, and the algorithm selection is made. And try to optimize the algorithm from two aspects of the algorithm implementation complexity and algorithm performance. (3) programming the KNN and SVM algorithms, using standard data sets to test its efficiency and performance, by tuning the relevant parameters. For example, the selection of K value in the KNN algorithm and the debugging of the related parameters in the SVM algorithm are used to optimize the algorithm. Finally, through the combined application of KNN and SVM, we can learn from each other and optimize the classification effect. (4) mastering the current frontier technology of VR. In the VIVE platform of HTC, the application scene of gesture recognition is constructed by using Unity game development engine, and the function module of data transmission and return result of related server is realized by Python language. Finally, the application of gesture recognition is realized.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;TP391.9
[Abstract]:In recent years, the rapid development of Internet and computer hardware has laid a solid foundation for the development of other areas of the Internet. Machine learning and artificial intelligence are hot spots in theoretical research. Virtual reality devices such as VR and AR have matured further in applications, and many companies have launched market-oriented product lines, although in artificial intelligence fault tolerance is not comparable to that of humans. But now our lives are surrounded by artificial intelligence products such as handwritten recognition in mobile phones and voice recognition in WeChat. The application of these intelligent scenes depends on the development of computer classification learning algorithms. The basic classification methods, such as KNN and SVM classification, have achieved satisfactory classification accuracy and fast classification speed after many years of research and application. KNN is a classical classification solution in the case of small amount of data, and SVM is also widely used in the field of two classification problems, which is complementary to each other to some extent. As shown above, the development of artificial intelligence not only brings convenience to users, but also greatly improves the user experience. Virtual reality devices and applications, such as VR and AR, have also received extensive attention and rapid development in the past two years. Hardware upgrades have enabled virtual reality to come into our lives, with HTC's virtual helmet VIVE and Microsoft's AR Glass Hololens attracting a lot of attention since its launch. Based on the analysis of domestic and international research status and various application practices in the field of virtual reality, this paper deeply studies the machine learning classification algorithm and the VIVE device application system of HTC. Spatial gesture recognition based on KNN algorithm is proposed and applied in VR environment. The research contents include: (1) explore how to solve the need of human-computer interaction in VR, how to narrow the communication distance between users and devices, so that users can immerse themselves more naturally in the scene constructed by virtual reality devices. In HTC's VIVE device, a mouse-like controller is used to allow the user to control its implementation, but this interaction often interferes with the user's normal use and the experience is not perfect. On the basis of this hardware device, this paper uses machine learning classification algorithm to build gesture recognition scene. (2) deeply study the principle and running mechanism of KNN and SVM classification algorithm, and deeply understand its basic application thought and code structure. On this basis, the advantages and disadvantages of different classification algorithms are analyzed, and according to the VR gesture recognition application to be constructed, the applicability of the algorithm is analyzed, and the algorithm selection is made. And try to optimize the algorithm from two aspects of the algorithm implementation complexity and algorithm performance. (3) programming the KNN and SVM algorithms, using standard data sets to test its efficiency and performance, by tuning the relevant parameters. For example, the selection of K value in the KNN algorithm and the debugging of the related parameters in the SVM algorithm are used to optimize the algorithm. Finally, through the combined application of KNN and SVM, we can learn from each other and optimize the classification effect. (4) mastering the current frontier technology of VR. In the VIVE platform of HTC, the application scene of gesture recognition is constructed by using Unity game development engine, and the function module of data transmission and return result of related server is realized by Python language. Finally, the application of gesture recognition is realized.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;TP391.9
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