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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/9067

    Title: 利用轉換狀態圖及模型建立進行人體運動姿勢辨識;Model based human motion recognition using transition diagram
    Authors: 吳居穆;Chu-Mu Wu
    Contributors: 資訊工程研究所
    Keywords: 視訊監控;目標物偵測;姿勢辨識;Video Surveillance;motion recognition;object detection
    Date: 2006-06-26
    Issue Date: 2009-09-22 11:40:29 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 電腦視覺的應用,一直都是相當熱門,也有許多相關的應用,近十年來,研究者對於辨識分析人體的動作或行為更是感興趣,目前可以應用的相關領域大致有虛擬實境:例如製作動畫角色的模擬;智慧型監控系統:可以節省人力的支出;高級人機介面:可以用手勢操作系統等。 而目前人體運動姿勢的辨識方法大致上可以分為兩類,第一種為模型建立方法,會將人體影像套上一個定義好的幾何模型或是輪廓模型,其缺點是容易受背景雜訊影響,需要有較良好的前景物影像來進行處理。第二種方式則是不需套入定義的人體模型,主要是利用輪廓比對,或是在輸入影像後,擷取特徵點來進行訓練和辨識,其缺點是特徵向量若太大,會使運算不易處理,也由於沒有事先定義好的運動模式,會不容易描述目前所進行的運動或行為。 基於上述的理由,本研究希望能夠結合兩種不同方式的優點,首先建立一個定義好的人體模型,接著在這個模型上,進行特徵的抽取,由於在定義的模型上擷取特徵,可以有效縮減特徵向量的複雜度,另外一方面,由於是利用擷取後的特徵向量來進行訓練和辨識,因此對於前景物影像,也可以有較穩定的處理效果;而人體運動大致上是平順進行轉換,例如直立走路,接著蹲下,然後再趴下或跌倒,而不會突然由趴下的動作突然變成直立行走,因此設計一個運動姿勢的轉換狀態圖,來描述實驗中運動姿勢的轉換,希望能夠提高辨識的準確度,實驗結果證明了本研究所提出之方法對於人體運動姿勢的辨識,具有良好的準確度。 During the past decade, the technique of computer vision has been widely applied in several fields. Typical applications include virtual reality, intelligent surveillance system, human-interface, etc. There are two categories of human motion recognition approaches including model based and non-model based. Model based approach usually fits the given image or blob to a shape model, which represents joint parts and human body parts. One has to segment images into different parts, such as head, torso, arms, and legs. The drawback of this approach is that it needs more stable foreground segmentation. As to non-model based approach, it extracts features from the image, and the correspondence between consecutive frames is obtained based on estimation or prediction of features relating to shape, texture, and colors. The drawback of this kind of approach is that it is difficult to define the activity because of the lacking of pre-defined model. In this thesis, the two approaches are combined. First, we use a pre-defied model, and features are extracted from different regions in this model. In this way, the complexity of features can be reduced due to the utilization of segmented images and the system can still perform well even if the foreground image is not stable. Human motions, like walking and crawling, usually transfer smoothly in each state. Hence, a transition diagram is designed to describe the transition between different motions. Experiments were conducted and results reveal the validity of our proposed approach.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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