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  • 學位論文

運用KINECT姿態辨識的使用者辨識研究

Using KINECT Gesture Recognition for User Recognition

指導教授 : 丁英智
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摘要


近年來,在智慧型環境中辨識安全系統慢慢受人們重視,而演化出許多身份辨識系統,本論文提出一種架構於KINECT骨架資訊於姿態辨識的使用者辨識系統,其中包含兩種型態的特徵值,分別為無學習方法的特徵型態以及經由學習方法所產生的學習特徵型態。基於人體骨架關節的三種特徵值,分別為相鄰關節距離、固定骨架角度、融合相鄰關節距離與固定骨架角度,以及兩種基於學習演算法特徵值,分別為重心偏移量(Gravity of Learning Offset, GLO)與轉移矩陣偏移量(Transfer Matrix of Learning Offset, TMLO),各作為使用者辨識演算法之特徵值。 本論文使用者辨識方法使用支撐向量機(Support Vector Machine, SVM)、高斯混合模型(Gaussian Mixture Model, GMM)、主成分分析法(Principal Component Analysis, PCA) ,並由這些方法發展出SVM使用者合法性確認、GMM及PCA使用者身分辨識,並提出以下三種架構之使用者辨識模型,PCA學習特徵之SVM使用者合法性確認、GMM-PCA並聯模型及PCA學習特徵之GMM使用者身分辨識,對傳統單一模型之使用者辨識作進一步性能改良。 本研究將三種無學習特徵值分別建立於SVM、GMM及PCA方法上,並以辨識率較好的特徵值提出運用於混合模型,以SVM和PCA選取相鄰關節距離特徵值、GMM則選取融合相鄰關節距離與固定骨架角度,將無學習特徵建立於GMM-PCA並聯模型並以雙模型累計正規化分數方法之共同決策判斷。 在使用者辨識時,動作會因為使用者的習慣與時間而改變,而影響每一次的辨識成效,在此以加入機器學習方法解決這個問題。本論文以相鄰關節距離為特徵值建立於PCA之中,再由PCA學習演算法發展出兩種基於PCA學習演算法的學習偏移量特徵值 ,各別為PCA-GLO與PCA-TMLO,並結合SVM以及GMM方法。PCA-GLO第16次學習建立於SVM的辨識率為94.3%,PCA-TMLO第10次學習建立於SVM的辨識率為98.9%,PCA-GLO第16次學習建立於GMM的辨識率為99.8%,由數據得知SVM的PCA-TMLO經學習後能超越傳統SVM辨識率,而學習次數辨識性能也優於SVM的PCA-GLO,而在GMM的PCA-GLO上則是在學習16次後能優於傳統GMM及PCA辨識率,以證實經由PCA學習方法求得的特徵值,具有學習之成效性。

關鍵字

KINECT 人體骨架關節 使用者辨識 SVM GMM PCA 機器學習

並列摘要


In recent years, the safe identification system used in intelligent environment has been attractive by people and more and more similarly systems were proposed. This paper presented a user identification based on posture and combined the skeleton data which gets from KINECT. It contains two types of features, including non-learning features and learning features of the learning methods. Based on human skeleton joints, there are three user features proposed by the author. The methods in sequence are “Adjacency Joint Distance”, “Confirm Skeleton Angle” and the last one is to combine of the above, and two learning features, “Gravity of Offset” (GLO), “Transfer Matrix of Offset” (TMLO). All of them were used in user identification system as the features. The paper are also using Support Vector Machine (SVM), Gaussian Mixture Model (GMM) and Principal Component Analysis (PCA) to develop user legality confirmed in SVM and develop user identity recognition in GMM and PCA. And propose three types of user recognition user recognition model, GMM-PCA、PCA learning-SVM、PCA learning-GMM, which trying to modify the original method of single model. Three types of non-learning features are separately training in SVM、GMM、PCA. And prefer to select features in better recognition rates. SVM and PCA we select “Adjacency Joint Distance” and GMM select combine features. Non-learning features was trained in GMM-PCA, total of score normalization GMM-PCA was decided to recognition result. The identification of action may change through the time and the habits of user, so it will affect the efficiency of each recognition process. To improve the situation, we add the learning method of machine and developed two learning algorithms. The paper is using Adjacency Joint Distance to train in PCA, and according to PCA learning methods to propose two types of learning offsets features, PCA-GLO and PCA-TMLO are training in SVM and GMM. PCA-GLO is learning 16 times training in SVM, the recognition rates was 94.3%. PCA-GLO is learning 16 times training in GMM, the recognition rates was 99.8%. And PCA-TMLO is learning 10 times training in SVM, the recognition rates was 98.9%. The experiment result, PCA-TMLO training in SVM was better than single SVM by more learning times, and learning times was better than PCA-GLO training in SVM. PCA-GLO training in GMM which recognition rates was better than single GMM. The experiment result, it proved the learning effect which the features was extracted in PCA learning methods.

並列關鍵字

KINECT skeleton joints user recognition SVM GMM PCA machine learning

參考文獻


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被引用紀錄


張育瑞(2016)。一種人體姿態命令辨識及其身份識別的強化式方法之研究〔碩士論文,國立虎尾科技大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0028-1608201616302200

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