本論文提出一個在視覺上新穎的即時偵測跌倒系統，我們這個系統能夠支援於各種攝影機視角的擺設，突破單一環境的限制。該系統的演算法藉由人體形狀與自然線索兩大特徵去區分跌倒與非跌倒的事件，並且有很高的準確率。對於人體形狀的分析上，我們提出一個利用影子來估測人體高度的方法，同時也結合HOG特徵以及行為能力特徵來加強整個系統的偵測能力。最後利用支援向量機(SVM)來做特徵的訓練以及串接式的分類來辨別跌倒行為。系統執行的結果是有效的應用在鳥瞰視角與一般視角，而我們的實驗結果也證明該系統可達到很高的偵測率及很低的計算量。

We present a novel real-time video-based human fall detection system in this thesis. Because the system is based on a combination of shadow-based features and various human postures, it can distinguish between fall-down and fall-like incidents with a high degree of accuracy. To support effective operation in different viewpoints, we propose a new feature called virtual height that can estimate the body height without 3D model reconstruction. As a result, the model is low computational complexity. Our experiment results demonstrate that the proposed system can achieve a high detection rate and a low false alarm rate.

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