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標題: 使用模糊類神經網路辨識人體姿態以及姿態估測
Huamn body posture recognition by Neural Fuzzy Netwrok and posture estimation
作者: 張家鳴
關鍵字: Human posture analysis
Center of gravity of huamn body
neural fuzzy network
出版社: 電機工程學系
摘要: 本論文提出兩種人體姿態分析法,其一為使用纇神經模糊網路作人體姿態辨識上,另一為利用輪廓來作人體姿態估測。 在人體姿態辨識上,我們辨識四種人體主要姿態,包含:站著,作著,躺著和彎著。我們運用移動物體切割演算法從背景中找出人體。再配合一連串影像處理技巧,包含中值濾波器,背景差值運算和型態學運算等等,來得到一完整的人體輪廓.。接下來,我們運用輪廓的水平與垂直投影向量以及離散傅立葉轉換(DFT)來取得投影向量的特徵值,並且配合物體的長寬比值,以這組數值代入類神經模糊網路運算,。在實驗中可發現出使用纇神經模糊運算的幫助下在判斷姿態上有著不錯的結果。並且我們將辨識結果應用在人突然倒地不起的偵測上。 在姿態估測上,主要在於估測出人體的頭,手端與腳端的位置。在已知人體的黑色輪廓下,我們取出人體的重心以及人體的週圍輪廓。由重心到人體輪廓的距離,取得一組距離曲線,找出曲線上的所有凸點,由所有凸點找出所要標示的位置。首先,我們利用人體重心方向角、身體結構與凸點曲線角來找出頭頂位置。再根據這個位置,判斷出腳和手等相對應的位置。實驗結果顯示,此法對大部份姿勢均可正確標示出位置。
This thesis proposes two human posture analysis methods, one is human body posture recognition by neural fuzzy network, and the other is human posture estimation by silhouette. For posture recognition, four kinds of main body postures, including standing, sitting, lying, and bending, are recognized. We use a moving object segmentation algorithm to distinguish the human body and background from a sequence images. After the human body is successfully segmented, we use a sequence of image processing algorithms, including median filter and morphological operation, to obtain a complete silhouette. We project the silhouette onto horizontal and vertical axes, respectively, and find Discrete Fourier Transform of each. Significant Fourier transform values together with length-width ratio of the silhouette are used as features. Recognizer is designed by a neural fuzzy network. Experimental results show that we can recognize the four postures with a high accuracy. Simulations on applying the recognition approach to the detection of the emergency condition that one suddenly falls down and can not stand up are also performed. In posture estimation, our objective is to locate significant body points, including head, tips of hand, and tips of feet. Based on the silhouette, we compute center of gravity (COG) of human body and boundary contour. By computing the distance between COG and pixels in the contour, we obtain a curve of distance. Concave points in the distance curve are located and regarded as candidates of the significant body points. Based on orientation of the body, body structure, and curvature of candidates, we first locate the hand followed by the location of tips of feet and tips of hand. Experiments show that the proposed approach can recognize significant points of most postures.
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