本文選題：手勢交互 + 樣本聚類��； 參考：《電子科技大學》2017年碩士論文

【摘要】：智能型的用戶界面操控技術日益受到重視,手勢交互的方式具有學習成本低、自然便捷和多樣豐富的特點,可以為操作者提供更為直觀、舒適的自然交互體驗。傳統的基于慣性傳感的手勢交互方法的研究焦點集中于如何使不依賴于個體的手勢識別方法更具有個體魯棒性,同時獲得更快的動態(tài)響應。但并未深入考慮算法中樣本集的正規(guī)性和有效性,在一定程度上影響算法的識別準確率。同時,當手勢復雜且手勢種類增多時,傳統方法更容易受到手勢信號中冗余信息及噪音信息的影響,造成手勢類別的誤判。針對傳統方法的不足與劣勢,為提高手勢識別的準確率和降低運算復雜度,本文進行了算法改進。實驗表明,本文方法的運算耗時較傳統DTW算法減少25%至31%,整體平均識別準確率在96.7%至98.84%,明顯優(yōu)于其他傳統算法。本文主要致力于以下三個方面的研究工作:1.針對傳統方法中樣本集構造問題,為改善樣本選取的非正規(guī)性,本文提出一種基于CDTW算法的樣本聚類訓練方法。該方法不僅可以克服不同個體手勢動作速度差異大的問題,還改善了樣本字典的正規(guī)性和有效性。通過樣本聚類獲得的典型樣本,在一定程度上壓縮了樣本集的大小,更重要的是包含了不同個體做各個不同手勢時的典型特征,令該方法具有更強的個體適應能力。2.針對傳統手勢交互方法在識別過程中運算量大的問題,聚類樣本的方法可以使這一問題得到一定改善,本文同時提出主軸分類思想,運算中測試手勢序列只與主軸相同的模板進行匹配,能夠有效減少在線模板匹配過程在整個手勢交互系統中的時間復雜度,從而確定其所屬類。3.針對傳統方法更容易受到手勢信號中冗余信息及噪音信息的影響從而造成手勢類別的誤判的問題,提出利用壓縮傳感方法作為手勢交互中識別過程的手段。利用這種壓縮降維的方法,既減少了運算量,又可以不失真地恢復手勢信號,保留重要特征,進而提高算法識別率,得出識別結果。
[Abstract]:Intelligent user interface manipulation technology has been paid more and more attention. Gesture interaction has the characteristics of low learning cost, convenience and diversity, which can provide operators with a more intuitive and comfortable experience of natural interaction. Traditional gesture interaction methods based on inertial sensing focus on how to make individual independent gesture recognition methods more robust and obtain faster dynamic response. However, the normality and validity of the sample set in the algorithm are not considered deeply, and the recognition accuracy of the algorithm is affected to some extent. At the same time, when the gestures are complicated and the kinds of gestures increase, the traditional methods are more vulnerable to the redundant information and noise information in the gesture signals, resulting in the false judgment of gesture types. In order to improve the accuracy of gesture recognition and reduce the computational complexity, the algorithm is improved to overcome the shortcomings and disadvantages of traditional methods. Experimental results show that the computational time of this method is 25% to 31% less than that of the traditional DTW algorithm, and the overall average recognition accuracy is from 96.7% to 98.84%, which is obviously superior to other traditional algorithms. This paper is devoted to the following three aspects of research work: 1. In order to improve the informality of sample selection, a training method of sample clustering based on CDTW algorithm is proposed to solve the problem of sample set construction in traditional methods. This method can not only overcome the problem of different individuals' gesture speed, but also improve the regularity and effectiveness of sample dictionary. The typical samples obtained by sample clustering can compress the size of the sample set to a certain extent, and more importantly, it contains the typical features of different individuals making different gestures, which makes the method have a stronger individual adaptability. 2. In view of the problem that the traditional gesture interaction method has a large amount of computation in the process of recognition, the clustering method can improve the problem to some extent. At the same time, this paper puts forward the idea of spindle classification. The test gesture sequence can only match the template with the same spindle, which can effectively reduce the time complexity of the online template matching process in the whole gesture interaction system, so as to determine its class. 3. Aiming at the problem that the traditional methods are more vulnerable to the redundant information and noise information in gesture signals resulting in the misjudgment of gesture categories, a compression sensing method is proposed as a means of recognition in gesture interaction. This method can not only reduce the computation, but also restore the gesture signal without distortion, keep the important features, and then improve the recognition rate of the algorithm and obtain the recognition results.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP212

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本文編號：1935360