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  本文選題：Kinect　切入點：姿勢識別　出處：《沈陽工業(yè)大學》2017年碩士論文

【摘要】：在機器視覺和圖像處理領域,關于人體姿勢的識別已經(jīng)成為一項重要的課題,并且在人機交互、虛擬現(xiàn)實和智能視頻監(jiān)控等領域得到廣泛的應用。然而目前仍有諸多問題沒有得到很好的解決,影響了計算機對于人體行為的理解。由于普通攝像機只能獲取到二維圖像,但二維信息到三維信息的重建會丟失很多重要數(shù)據(jù),影響動作識別的精度。盡管科研人員設計了多種圖像重構(gòu)算法,但是仍無法避免光照、紋理遮擋等影響。而Kinect傳感器使用一種新的獲取圖像的方式,它通過一對紅外攝像頭捕獲到帶有空間距離的深度圖像,并且在深度圖像的基礎上提取出含有三維坐標信息的骨骼數(shù)據(jù)流。但是Kinect沒有給出姿勢識別的高級函數(shù),原因在于人體動作千變?nèi)f化,很難構(gòu)建出一套通用的模型進行識別。為了提升動作識別的效果,本文使用Kinect傳感器來獲取到人體20個骨骼關節(jié)點的三維坐標,并且根據(jù)人體姿態(tài)的特征,以關節(jié)點的相對距離和角度序列為特征參數(shù)。在靜態(tài)姿勢的識別中,通過特征向量對樣本集進行訓練,并使用KNN算法作為分類器對姿勢進行識別。在動作評價中,在分析了運動特征序列的時間特性以后,采用線性回歸的方法對樣本曲線進行訓練,使用最小二乘法擬合出一條最佳角度曲線作為標準模板,在考慮到曲線之間時間序列長短不一的問題,通過DTW算法對不同長度的關節(jié)角度曲線進行匹配,并且通過定義一套公式對動作進行評價,以曲線之間DTW差值作為實驗參數(shù),最終將評價方法應用到動作打分的體感游戲中。本文通過實驗分析了相對距離和關節(jié)點角度作為動作識別特征向量的可行性。選取簡氏太極拳其中的4式作為靜態(tài)姿勢識別對象,實驗結(jié)果證明通過該方法進行姿態(tài)識別可以獲得較高的識別率。然后又對動作評價的實驗數(shù)據(jù)進行分析,在總結(jié)8個角度的DTW差值樣本點分布規(guī)律之后,定義一套公式對動作進行評價,并設計動作評估系統(tǒng)論證該動作評價方法的合理性。由于評價公式中的基數(shù)和因子會隨著動作的變換而不斷重新計算,增加了該評價方法的復雜度,接下來的工作是完善動作評價公式的各項參數(shù)使評價方法具有更優(yōu)的效率和適應性。
[Abstract]:In the field of machine vision and image processing, recognition of human posture has become an important issue, and in human-computer interaction, Virtual reality and intelligent video surveillance are widely used. However, there are still many problems that have not been solved well, which affect the understanding of human behavior by computer. But the reconstruction of two-dimensional to three-dimensional information can lose a lot of important data and affect the accuracy of motion recognition. Although researchers have designed a variety of image reconstruction algorithms, but still can not avoid lighting, The Kinect sensor uses a new way to capture images, which capture depth images with spatial distances through a pair of infrared cameras. On the basis of the depth image, the skeletal data stream with three-dimensional coordinate information is extracted. But Kinect does not give a high-level function of posture recognition, because the human body's actions vary greatly. It is very difficult to construct a universal model for recognition. In order to improve the effect of motion recognition, we use Kinect sensor to get the three-dimensional coordinates of 20 skeletal joints, and according to the characteristics of human posture, The relative distance and angle sequence of the node are taken as the characteristic parameters. In the recognition of static pose, the sample set is trained by the feature vector, and the posture is recognized by using KNN algorithm as the classifier. After analyzing the time characteristics of motion feature series, the linear regression method is used to train the sample curve, and the least square method is used to fit an optimal angle curve as the standard template. Considering the difference of time series between curves, the joint angle curve of different length is matched by DTW algorithm, and the action is evaluated by defining a set of formulas. The difference of DTW between curves is taken as the experimental parameter. Finally, the evaluation method is applied to the body feeling game of action scoring. The feasibility of using relative distance and the angle of gate node as the feature vectors of action recognition is analyzed experimentally in this paper. Four of the four forms of Taijiquan are selected as static. State posture recognition object, The experimental results show that the attitude recognition rate can be obtained by this method. Then, the experimental data of motion evaluation are analyzed, and the distribution of DTW difference sample points from 8 angles is summarized. A set of formulas is defined to evaluate the action, and a motion evaluation system is designed to demonstrate the rationality of the evaluation method. The complexity of the evaluation method is increased. The next work is to improve the parameters of the action evaluation formula to make the evaluation method more efficient and adaptive.
【學位授予單位】：沈陽工業(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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