Learning-based human fall detection using rgb-d cameras

Szu-Hao Huang*, Ying Cheng Pan

*此作品的通信作者

研究成果: Conference contribution同行評審

6 引文 斯高帕斯(Scopus)

摘要

Automatic detection of human fall events is a challenging but important function of the real-time surveillance system. The goal of the proposed system is to develop a frame-by-frame fall detection system based on real-time RGB-D camera devices. The proposed system is composed of a complex off-line learning stage which combines several novel machine learning techniques and a series of on-line detection processes. A background subtraction method based on iterative normalized-cut segmentation algorithm is proposed to identify the pixel-wise human regions rapidly. The silhouettes are extracted to measure the pose similarity between different samples. Manifold learning algorithm reduces the feature dimensions and several discriminant analysis techniques are applied to model the final human fall detector. The experimental database contains 65 color video and corresponding depth maps. The experimental results based on a leave-one-out cross-validation testing show that our proposed system can detect the fall events effectively and efficiently.

原文English
主出版物標題Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
發行者MVA Organization
頁面439-442
頁數4
ISBN(列印)9784901122139
出版狀態Published - 20 5月 2013
事件13th IAPR International Conference on Machine Vision Applications, MVA 2013 - Kyoto, Japan
持續時間: 20 5月 201323 5月 2013

出版系列

名字Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013

Conference

Conference13th IAPR International Conference on Machine Vision Applications, MVA 2013
國家/地區Japan
城市Kyoto
期間20/05/1323/05/13

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