本論文應用影像處理技術於偵測跌倒事件以及事件發生後無法自行起身與可以自行起身的情況，將偵測系統利用電腦視覺技術於畫面上，以期許當事件發生時可以提供警告訊息。在本論文中，先將人體的區域(移動物)找出，再利用本論文提出的三階段式判斷法則來偵測是否跌倒以及無法自行起身的情況；第一階段判斷法則為，針對在正常走路的情況與跌倒的情況下，利用軸向角度上的變化來判別；第二階段判斷法則為，針對跌倒與系統中會產生誤判成跌倒的彎腰蹲下動作，利用三種特徵值配合倒傳遞類神經分類器的使用，來將此兩種情況分類；第三階段判斷法則為，當第二階段判斷法則判別為跌倒時並且躺平或者趴著情況下，接著利用軸向角度變化來判別無法自行起身與可以自行起身的狀況。在第一、二階段判斷法則中，分別有16組無跌倒動作(包含走路與彎腰蹲下)與 42組跌倒動作(包含前跌與後跌)影片，系統偵測成功辨識率為94.8%；在第三階段判斷法則中，分別各有8組跌倒後且無法自行起身與跌倒後但可以自行起身動作影片，系統偵測成功辨識率為100%，因此可看出本論文在偵測本實驗所探討的動作上成功率皆可以達到不錯的效果。

This thesis applies image processing techniques to detect a person who falls down and is unable to get up by oneself after fall event. In this thesis, when the event is detected by the detection system, a warning message can be provided to surveillance officers in a computer-based vision system. In the detection system, at first, the motion detection is used to find the moving object (human). Next, we propose three stage judgment rules to detect fall event. In the first stage, orientation variation of the object is used to determine the walking and falling gestures. In the second stage, back propagation neural network (BPNN) with three feature inputs is used to detect fall and stoop or squat situations. In the third stage, we compute orientation variation of the object for detecting disability to get up by oneself and get-up action after lying on the ground. In the experiment, fifty eight video sequences include sixteen normal events (walk and stoop or squat) and forty two fall events (forward fall and backward fall) for the first stage and second stage judgment rules, and sixteen video sequences include eight for disability to get up by oneself after fall event and eight for get-up action from the ground. The recognition rates of two experiments are 94.8% and 100%, respectively. The results show that the proposed fall detection system can do good job in recognizing fall events.

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