基于特征提取的掌心實時定位與手勢識別算法的研究
發(fā)布時間：2018-11-16 10:31
【摘要】：隨著計算機視覺技術(shù)的深入發(fā)展,通過鼠標(biāo)、鍵盤作為輸入接口的傳統(tǒng)交互模式已然無法滿足人們的需求,用戶直接與計算機交流的自然人機交互才是理想的人機交互模式。手勢識別作為以人為中心的更自然的人機交互方式,能夠滿足用戶與虛擬環(huán)境之間的直接交互,在智能家居、體感游戲、啞語識別等多個領(lǐng)域都有廣泛應(yīng)用。因此,實時手勢識別技術(shù)具有重要的學(xué)術(shù)價值和應(yīng)用前景。現(xiàn)有的手勢識別研究仍然存在諸多不足:訓(xùn)練固定手勢模板的識別方法難以滿足實時性交互需求;單一基于手部膚色特征提取的手勢識別方法在手勢分割時容易出現(xiàn)分割不完整,導(dǎo)致識別率不高;基于可穿戴設(shè)備的手勢識別方法要求用戶必須佩戴數(shù)據(jù)手套等設(shè)備,此類設(shè)備價格昂貴且不利于推廣。本論文主要針對手勢識別算法中的手勢區(qū)域膚色分割、手勢特征提取、數(shù)字手勢識別三大部分進行了詳細研究,主要工作如下:首先在手勢區(qū)域分割階段,研究了普通攝像頭平臺下采集圖像未出現(xiàn)人臉和出現(xiàn)人臉兩種情況下的分割方法。當(dāng)攝像頭采集圖像未出現(xiàn)人臉時通過閾值化膚色檢測器直接提取手勢區(qū)域;當(dāng)攝像頭采集圖像出現(xiàn)人臉時,根據(jù)人臉與手部膚色一致性原則,提出了融合人臉膚色檢測的手勢區(qū)域分割新方法。該方法通過人臉檢測獲取人臉區(qū)域膚色像素值范圍,再與傳統(tǒng)閾值法相結(jié)合,通過雙閾值法準(zhǔn)確的分割背景和手勢區(qū)域,可以在一定程度上優(yōu)化分割效果。其次在手勢特征提取階段,采用運動目標(biāo)圖像檢測方法,在兩幀幀間差分法的基礎(chǔ)上,采用三幀幀間差法結(jié)合膚色分割來實現(xiàn)運動手勢區(qū)域檢測。根據(jù)本文算法提取了手勢輪廓、手勢凸包與凸缺陷、指尖與指間凹槽等重要手勢特征,并在此基礎(chǔ)上提出了基于指間凹槽最小外接圓的實時掌心檢測定位方法,準(zhǔn)確有效的實現(xiàn)了手勢掌心的定位。該方法適應(yīng)手勢區(qū)域進行平移、旋轉(zhuǎn)、翻轉(zhuǎn)等不同場景下的識別特征提取要求,具有較好的魯棒性。最后在數(shù)字手勢識別階段,根據(jù)提取到的指尖與指間凹槽和掌心等特征,構(gòu)造出識別決策樹模型,實現(xiàn)了對常用的數(shù)字手勢快速準(zhǔn)確識別。搭建的基于VS 2013集成開發(fā)平臺的實時手勢識別系統(tǒng)驗證了上述方法的有效性。
[Abstract]:With the development of computer vision technology, the traditional interaction mode of mouse and keyboard as input interface can no longer meet the needs of people. The natural human-computer interaction between users and computers is the ideal human-computer interaction mode. Gesture recognition, as a more natural human-computer interaction mode, can satisfy the direct interaction between users and virtual environment. It has been widely used in many fields such as smart home, body sense game, mute recognition and so on. Therefore, real-time gesture recognition technology has important academic value and application prospect. The existing research on gesture recognition still has many shortcomings: the recognition method of training fixed gesture template is difficult to meet the real-time interaction requirements; Hand gesture recognition method based on hand skin color feature extraction is prone to incomplete hand gesture segmentation, which leads to low recognition rate. Gesture recognition based on wearable devices requires users to wear data gloves and other devices, which are expensive and unsuitable for promotion. In this paper, the skin color segmentation of gesture region, gesture feature extraction and digital gesture recognition are studied in detail. The main work is as follows: firstly, in the phase of gesture region segmentation, In this paper, the segmentation method of image without face and human face is studied on the common camera platform. When the face is not seen in the image captured by the camera, the gesture area is directly extracted by the threshold skin color detector. According to the principle of consistency between the face and the skin color of the hand, a new method of hand gesture region segmentation based on facial color detection is proposed. This method obtains the range of skin color pixels of face region by face detection and combines with the traditional threshold method. By using double threshold method to segment the background and gesture regions accurately the segmentation effect can be optimized to a certain extent. Secondly, in the phase of gesture feature extraction, the moving target image detection method is adopted, and based on the difference method between two frames, the three-frame inter-frame difference method combined with skin color segmentation is used to realize the motion gesture region detection. According to this algorithm, some important gesture features, such as gesture contour, gesture convex hull and convex defect, finger tip and finger grooves, are extracted. Based on this, a real-time palm detection and localization method based on the minimum circumscribed circle of interdigital groove is proposed. Accurate and effective hand gesture palm positioning. This method adapts to the requirements of feature extraction in different scenes, such as translation, rotation and flipping, and has good robustness. Finally, in the phase of digital gesture recognition, a decision tree model is constructed according to the extracted features such as grooves and palms between fingertips and fingers, and the recognition of common digital gestures is realized quickly and accurately. A real-time gesture recognition system based on VS 2013 integrated development platform is built to verify the effectiveness of the above method.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
[Abstract]:With the development of computer vision technology, the traditional interaction mode of mouse and keyboard as input interface can no longer meet the needs of people. The natural human-computer interaction between users and computers is the ideal human-computer interaction mode. Gesture recognition, as a more natural human-computer interaction mode, can satisfy the direct interaction between users and virtual environment. It has been widely used in many fields such as smart home, body sense game, mute recognition and so on. Therefore, real-time gesture recognition technology has important academic value and application prospect. The existing research on gesture recognition still has many shortcomings: the recognition method of training fixed gesture template is difficult to meet the real-time interaction requirements; Hand gesture recognition method based on hand skin color feature extraction is prone to incomplete hand gesture segmentation, which leads to low recognition rate. Gesture recognition based on wearable devices requires users to wear data gloves and other devices, which are expensive and unsuitable for promotion. In this paper, the skin color segmentation of gesture region, gesture feature extraction and digital gesture recognition are studied in detail. The main work is as follows: firstly, in the phase of gesture region segmentation, In this paper, the segmentation method of image without face and human face is studied on the common camera platform. When the face is not seen in the image captured by the camera, the gesture area is directly extracted by the threshold skin color detector. According to the principle of consistency between the face and the skin color of the hand, a new method of hand gesture region segmentation based on facial color detection is proposed. This method obtains the range of skin color pixels of face region by face detection and combines with the traditional threshold method. By using double threshold method to segment the background and gesture regions accurately the segmentation effect can be optimized to a certain extent. Secondly, in the phase of gesture feature extraction, the moving target image detection method is adopted, and based on the difference method between two frames, the three-frame inter-frame difference method combined with skin color segmentation is used to realize the motion gesture region detection. According to this algorithm, some important gesture features, such as gesture contour, gesture convex hull and convex defect, finger tip and finger grooves, are extracted. Based on this, a real-time palm detection and localization method based on the minimum circumscribed circle of interdigital groove is proposed. Accurate and effective hand gesture palm positioning. This method adapts to the requirements of feature extraction in different scenes, such as translation, rotation and flipping, and has good robustness. Finally, in the phase of digital gesture recognition, a decision tree model is constructed according to the extracted features such as grooves and palms between fingertips and fingers, and the recognition of common digital gestures is realized quickly and accurately. A real-time gesture recognition system based on VS 2013 integrated development platform is built to verify the effectiveness of the above method.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
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2 ;新型手勢識別技術(shù)可隔著口袋操作手機[J];電腦編程技巧與維護;2014年07期
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