本文選題：計算機視覺　切入點：手勢檢測　出處：《西南交通大學(xué)》2017年碩士論文

【摘要】：在目前人工智能高速發(fā)展的時代,對計算機視覺的研究也越來越熱門。在視覺領(lǐng)域中,由于人手手勢表達能力的豐富性,針對手勢識別的研究者不斷增多。隨著人們對手勢識別研究的深入,使得人機交互更加人性化。目前機器的研究不斷趨于小型化,然而外部輸入設(shè)備一直占了機器的很大一部分,基于計算機視覺的手勢識別,使得機器去掉這些外部輸入設(shè)備成為可能。目前國內(nèi)對手勢識別的研究,很大一部分是對一些簡單手勢的識別,手勢量較少,為了更好、更簡單的實現(xiàn)人機交互,利用普通攝像頭實時采集人手圖像,完成對26個英文字母手勢的檢測、跟蹤和識別,并且輸出相應(yīng)的字母。通過對相關(guān)算法的分析和改進,使得效果具有一定的改善。首先,對于人手的檢測,膚色分割檢測是最簡單而且有效的方法,但是膚色檢測很容易誤檢,例如把人臉誤檢為人手。利用圖像的Haar特征,以Adaboost分類器進行目標檢測在較大尺寸圖像的圖像上檢測比較困難,所以利用兩種方法的優(yōu)點,把膚色檢測的結(jié)果輸入Adaboost分類器進行檢測,很好的完成人手檢測,提高了檢測精度。其次,在人手跟蹤上,粒子濾波跟蹤算法具有不錯的效果,但基本粒子濾波跟蹤算法在重采樣階段存在粒子退化和粒子匱乏等缺點,針對此缺點,提出了一種基于風(fēng)驅(qū)動優(yōu)化的粒子濾波改進算法,既在粒子濾波算法重采樣前,引入風(fēng)驅(qū)動優(yōu)化算法對粒子進行優(yōu)化,仿真和實驗結(jié)果表明該改進算法在一定程度上提高了基本粒子濾波跟蹤算法的效果。然后,對實時跟蹤到的手勢區(qū)域,進行識別。識別方法主要采用深度學(xué)習(xí)——卷積神經(jīng)網(wǎng)絡(luò)進行識別,針對卷積神經(jīng)網(wǎng)絡(luò)識別率低和誤識別率高的手勢利用模板匹配的方法進行驗證,從而提高了整體手勢的識別效率。最后,完成了實時手勢識別系統(tǒng)設(shè)計,該系統(tǒng)通過攝像頭采集視頻圖像,完成字母手勢檢測、跟蹤和識別,同時把相應(yīng)的手勢識別結(jié)果以英文字母的形式輸出,實現(xiàn)了一種手勢輸入法。
[Abstract]:In the era of the rapid development of artificial intelligence, the research on computer vision is becoming more and more popular. In the field of vision, due to the richness of hand gesture expression, The number of researchers for gesture recognition is increasing. With the development of hand gesture recognition, the human-computer interaction becomes more and more humanized. At present, the research of machine is becoming more and more miniaturized. However, the external input devices have always occupied a large part of the machine. Gesture recognition based on computer vision makes it possible for the machine to remove these external input devices. A large part is the recognition of some simple gestures, the amount of gestures is less, in order to better, more simple to achieve human-computer interaction, the use of ordinary cameras real-time acquisition of human images, to complete 26 letters of hand gesture detection, tracking and recognition. And output the corresponding letters. Through the analysis and improvement of the related algorithm, the effect is improved. Firstly, for the manual detection, skin color segmentation detection is the simplest and most effective method, but the skin color detection is easy to misdetect. For example, using the Haar feature of the image and using the Adaboost classifier to detect the target on the image of large size image is more difficult, so the advantages of the two methods are used. The result of skin color detection is input into Adaboost classifier for detection, which completes the manual detection well and improves the detection accuracy. Secondly, in the manual tracking, particle filter tracking algorithm has a good effect. However, the basic particle filter tracking algorithm has the shortcomings of particle degradation and particle scarcity in the resampling stage. In view of this shortcoming, an improved particle filter algorithm based on wind driven optimization is proposed, which is prior to the particle filter algorithm resampling. The wind driven optimization algorithm is introduced to optimize the particle. The simulation and experimental results show that the improved algorithm improves the performance of the basic particle filter tracking algorithm to some extent. The recognition method is mainly based on deep learning-convolution neural network, and the method of template matching is used to verify the low recognition rate and high error recognition rate of convolutional neural network. Finally, the design of real-time gesture recognition system is completed. The system collects video images through the camera, completes the letter gesture detection, tracking and recognition. At the same time, the corresponding gesture recognition results are output in the form of English letters, and a gesture input method is realized.
【學(xué)位授予單位】：西南交通大學(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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