近年來，全球人口趨於老年化，自動偵測居家跌倒行為成為一個相當重要的議題。在本論文中，藉由二維影像萃取特徵的方式，解析彩色影像中的虛擬高度、物件外形、物件特徵點的多維特徵，搭配學習的演算法—隱藏式條件機率域(Hidden Conditional Radom Fields)，來辨識家庭中常有的行為，如走路、坐下、蹲下、跌倒。尤其是分辨出類跌倒的坐下、蹲下與跌倒，三個確切類別。
藉由二維影像萃取特徵的特徵，總共可以分成三類，第一類是擷取人在畫面中的陰影以預測虛擬高度；第二類是使用GIST描述人的外觀輪廓；第三類是使用空間金字塔匹配(Spatial Pyramid Matching)描述人的外觀特徵點。採用隱藏式條件隨機場的架構融合三種特徵，並且考慮到特徵於序列間的前後關係，加強各類別影像序列的識別準確率。本論文還比較了隱藏式馬可夫模型(Hidden Markov Model)、稀疏時空特徵(Cuboids)於跌倒與非跌倒的行為辨識。其辨識率與二者相比都有不錯的表現。

In recent years, the global population has begun to age rapidly. Automatic fall detection for senior citizens has become an important issue for smart home. In this research, we propose a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on Hidden Conditional Random Fields model, and an intelligent combination of height estimation and appearance cues. Our system can efficiently distinguish “fall-down incidents” from “fall-like incidents” such as sit-down and squat. Experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate. Also, the proposed system outperforms Hidden Markov Chain and Cuboids in terms of detection rate.

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