本文選題：手勢識別 + 二值化網(wǎng)絡�。� 參考：《湖南工業(yè)大學》2017年碩士論文

【摘要】：手勢識別是人機交互一個重要的研究課題,由于對它的研究特別是對基于視覺的手勢識別的研究順應了近年來人機交互從機器友好型向著人類友好型發(fā)展的趨勢,因此有著極大的科研和應用前景。然而在實際使用中,人手形態(tài)的多樣性,及其所處環(huán)境的背景、光線的變化等因素都給計算機從圖像信息中正確識別的手勢帶來了極大挑戰(zhàn)。針對這些問題,本文分別對手勢識別、手勢檢測等問題進行了研究,主要工作如下:(1)針對手勢檢測問題,結(jié)合視頻中的多種檢測算法提出了一種多策略融合的手勢檢測方法。為了解決復雜背景下手勢檢測出現(xiàn)的誤檢問題,研究了膚色檢測、vibe運動檢測等算法的原理,根據(jù)各種算法在檢測中的特點在將膚色、運動和人臉信息進行融合,提升了在復雜背景下手勢檢測的魯棒性。特別的針對手勢與類膚色背景重合時的檢測容易失效問題,對融合策略進行了自適應閾值的改進,改善了算法在該種情況下的檢測率。(2)針對手勢分類識別問題,在普通的深度學習卷積神經(jīng)網(wǎng)絡手勢識別方法的基礎上提出了一種基于二值卷積神經(jīng)網(wǎng)絡的手勢識別方法。該方法將網(wǎng)絡的二值化方法與卷積神經(jīng)網(wǎng)絡手勢識別方法相結(jié)合,使用二值化后的權(quán)值提替代網(wǎng)絡中原本的高精度權(quán)值,減少了算法計算量及內(nèi)存占用。通過實驗證明,算法在取得了足夠的準確性和魯棒性的基礎上,計算效率和在實時系統(tǒng)中的適用性得到了提升。(3)設計和實現(xiàn)了一個手勢識別系統(tǒng),展示了手勢識別在人機交互系統(tǒng)中的應用。從系統(tǒng)的需求和功能模塊的設計,到結(jié)合了前面提出的兩種方法的復雜背景下的手勢識別功能模塊及手勢訓練模塊的實現(xiàn),再到將成熟的人臉識別檢測方案集成的協(xié)同認證模塊的實現(xiàn),本文詳細地介紹了系統(tǒng)設計實現(xiàn)的各個細節(jié)。最后通過實驗展示了系統(tǒng)用于識別數(shù)字和解鎖的功能和特性。
[Abstract]:Gesture recognition is an important research topic in human-computer interaction. Because of its research, especially the research on visual gesture recognition, it conforms to the trend of human-computer interaction from machine-friendly to human-friendly in recent years. Therefore, there is a great prospect of scientific research and application. However, in practical use, the diversity of the human hand shape, the background of the environment and the change of light bring great challenges to the correct recognition of hand gestures from the image information by the computer. Aiming at these problems, this paper studies the problems of gesture recognition and gesture detection respectively. The main work is as follows: (1) aiming at the problem of hand gesture detection, a multi-strategy fusion method for gesture detection is proposed in combination with a variety of video detection algorithms. In order to solve the problem of false detection in hand gesture detection in complex background, the principle of skin color detection and motion detection is studied. According to the characteristics of the algorithms in detection, the color, motion and face information are fused. The robustness of hand gesture detection in complex background is improved. Especially, aiming at the problem that the detection is easy to fail when the gesture and the similar skin color background coincide, the adaptive threshold of the fusion strategy is improved, and the detection rate of the algorithm in this case is improved. On the basis of common deep learning convolution neural network gesture recognition method, a gesture recognition method based on binary convolution neural network is proposed. This method combines the binarization method of the network with the hand gesture recognition method of convolution neural network, and uses the binary weight value to replace the original high precision weight value in the network, thus reducing the computational complexity and memory footprint of the algorithm. It is proved by experiments that the algorithm has achieved enough accuracy and robustness, and the computational efficiency and applicability in real-time system have been improved. (3) A hand gesture recognition system is designed and implemented. The application of gesture recognition in human-computer interaction system is demonstrated. From the design of the system requirements and function modules to the implementation of the hand gesture recognition function module and the gesture training module under the complex background of the two methods mentioned above, Then to the implementation of the collaborative authentication module which integrates the mature face detection scheme, this paper introduces the details of the system design and implementation in detail. Finally, the functions and characteristics of the system for identifying numbers and unlocking are demonstrated through experiments.
【學位授予單位】：湖南工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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