A Robust Fall Detection Scheme Using Human Shadow and SVM Classifiers
碩士 === 國立臺灣科技大學 === 電子工程系 === 100 === 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 degre...
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| Format: | Others |
| Language: | zh-TW |
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2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/12696932794927637870 |
| Summary: | 碩士 === 國立臺灣科技大學 === 電子工程系 === 100 === 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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