本文關(guān)鍵詞： Kinect 手勢(shì)識(shí)別 加權(quán)動(dòng)態(tài)時(shí)間規(guī)整算法 智能家居　出處：《遼寧科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著社會(huì)的不斷發(fā)展,生活水平的不斷提升,智能家居行業(yè)也隨之迅猛發(fā)展。但是,各式各樣的設(shè)備功能給用戶帶來(lái)交互體驗(yàn)不佳和操作繁瑣的問(wèn)題也更加明顯。相比于傳統(tǒng)人機(jī)交互系統(tǒng)使用鼠標(biāo)、鍵盤等操作方式,更加簡(jiǎn)單、自然且人性化的手勢(shì)識(shí)別技術(shù)在人機(jī)交互中正扮演著重要的角色。為了解決當(dāng)前智能家居系統(tǒng)中存在的操作繁瑣,用戶不能通過(guò)自然的方式與機(jī)器進(jìn)行交互而導(dǎo)致較差的用戶體驗(yàn)等問(wèn)題,本文將基于Kinect的手勢(shì)識(shí)別技術(shù)融入至設(shè)計(jì)的智能家居人機(jī)交互系統(tǒng),其使用Kinect獲取人體圖像的深度信息與骨骼數(shù)據(jù)。研究了基于相對(duì)位置算法、隱馬爾可夫模型算法、動(dòng)態(tài)時(shí)間規(guī)整算法和加權(quán)動(dòng)態(tài)時(shí)間規(guī)整的模板匹配手勢(shì)識(shí)別算法,并采用基于加權(quán)動(dòng)態(tài)時(shí)間規(guī)整的模板匹配手勢(shì)識(shí)別算法提取預(yù)先定義的手勢(shì)動(dòng)作。實(shí)際的手勢(shì)實(shí)驗(yàn)結(jié)果表明:使用該算法實(shí)現(xiàn)手勢(shì)識(shí)別是可行且有效的,其最佳識(shí)別位置是在Kinect的正前方2到2.5米,識(shí)別準(zhǔn)確率達(dá)到98%左右。此外,研究了智能家居系統(tǒng)的管理流程與系統(tǒng)硬件模塊,設(shè)計(jì)了硬件電路與相應(yīng)的模塊軟件,并采用Zig Bee技術(shù)實(shí)現(xiàn)了手勢(shì)控制指令的傳輸。本文工作主要集中在研究了基于Kinect的四種手勢(shì)識(shí)別算法,最終采用基于加權(quán)動(dòng)態(tài)時(shí)間規(guī)整的模板匹配手勢(shì)識(shí)別算法提取手勢(shì)動(dòng)作,使用C#.NET語(yǔ)言編程實(shí)現(xiàn)了基于Kinect手勢(shì)識(shí)別的人機(jī)交互系統(tǒng);并完成了智能家居系統(tǒng)硬件模塊電路設(shè)計(jì)及相應(yīng)軟件功能實(shí)現(xiàn);熟悉并應(yīng)用RS485、I2C總線、CAN總線及Zig Bee協(xié)議并應(yīng)用到控制系統(tǒng)中。
[Abstract]:With the development of society and the improvement of living standard, the smart home industry is also developing rapidly. All kinds of device functions bring users a more obvious problem of poor interaction experience and cumbersome operation. Compared with the traditional man-machine interaction system using mouse, keyboard and other operating methods, it is much simpler. The natural and humanized gesture recognition technology is playing an important role in human-computer interaction. The user can not interact with the machine in a natural way, which leads to poor user experience. In this paper, the gesture recognition technology based on Kinect is integrated into the intelligent home human-computer interaction system. Kinect is used to obtain depth information and bone data of human body image. Based on relative position algorithm, hidden Markov model algorithm, dynamic time warping algorithm and weighted dynamic time warping algorithm, template matching gesture recognition algorithm is studied. A template matching gesture recognition algorithm based on weighted dynamic time regularization is used to extract predefined gesture actions. The experimental results show that the algorithm is feasible and effective. The best recognition position is 2 to 2. 5 meters in front of Kinect, and the recognition accuracy is about 98%. In addition, the management flow and system hardware module of smart home system are studied, and the hardware circuit and corresponding module software are designed. This paper mainly focuses on the research of four gesture recognition algorithms based on Kinect, and finally uses the template matching gesture recognition algorithm based on weighted dynamic time regularization to extract gesture actions. The human-computer interaction system based on Kinect gesture recognition is realized by using C#.NET language, and the hardware module circuit design and corresponding software function realization of smart home system are completed. Familiar with can bus and Zig Bee protocol and apply to control system.
【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TU855

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本文編號(hào)：1532371