結合人體形狀特徵與運動歷史之跌倒偵測系統之研究

Abstract

近年來老人照護的議題越來越受到重視，因此有關跌倒偵測系統的研究蓬勃發展。本論文利用影像處理的方式提出一套跌倒偵測系統。藉由影像中行人的運動狀態以及人體的形狀特徵，於監控環境中偵測是否有跌倒行為發生並發出警訊通報相關人員，達到縮短傷患等待就醫的時間，提高恢復甚至存活的機率。 在此，本論文的貢獻有以下兩點：第一，本論文利用前景資訊以及人體形狀提取出一組特徵，並透過訓練分類器應用於跌倒偵測上，使跌倒偵測有更準確的成效；第二，藉由運動歷史影像，搭配本論文所使用的人體形狀特徵，完成一個跌倒偵測系統的雛型，並且能有效分辨正常行為如躺下、坐下、蹲下與跌倒行為的狀態。 藉由實驗結果證明本論文提出之方法能增加跌倒偵測的效能，並且利用影片驗證本系統能持續在多個不同的動作中有效地進行跌倒行為的偵測，最後也增加加速度資訊整合於本論文提出之系統，透過影像與加速度資訊的配合達到提升系統偵測的準確率。

The issue of health care of elder is more important in recent years. Therefore, the research of fall detection system is very flourishing. In this thesis, we propose a fall detection system based on image processing. This system can detect falls of people in surveillance environment by motion and human shape in image. When system detect fall, it will send message to reduce waiting time and increase the survival rate. There are two contributions of this thesis. First, we proposes a set of features by foreground and human shape. Train a classifier make the result of fall detection system more accurately. Finally, we completes the prototype of system in fall detection base on our proposed feature and motion history image, and the system can distinguish between falls and normal activities such as sitting, lying or crouching. From experimental results, our proposed method can increase efficacy of fall detection. We also prove our system can detect fall in several continuous movement by video. Finally, we integrate our proposed system with accelerometer to increase the accuracy of fall detection system.