A Novel Shadow-Assistant Human Fall Detection Scheme Using a Cascade of SVM Classifiers
Visual recognition of human fall incidents in video clips has been an active research issue in recent years, However, most published methods cannot effectively differentiate between fall-down and fall-like incidents such as sitting and squatting. In this paper, we present a novel shadow-assistant me...
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Published in | Structural, Syntactic, and Statistical Pattern Recognition pp. 710 - 718 |
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Main Authors | , , |
Format | Book Chapter |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | Visual recognition of human fall incidents in video clips has been an active research issue in recent years, However, most published methods cannot effectively differentiate between fall-down and fall-like incidents such as sitting and squatting. In this paper, we present a novel shadow-assistant method for detecting human fall. Normally, complex 3-D models are used to estimate the human height. However, to reduce the high computational cost, only the information of moving shadow is used for this context. Because the system is based on a combination of shadow-assistant height estimation, and a cascade of SVM classifiers, it can distinguish between fall-down and fall-like incidents with a high degree of accuracy from very short sequence of 1-10 frames. Our experimental results demonstrate that under bird’s-eye view camera setting, the proposed system still can achieve 100% detect rate and a low false alarm rate, while the detection rate of other fall detection schemes have been dropped dramatically. |
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Bibliography: | This work was supported by National Science Council of R.O.C. under contract NSC 100-222-E-011-134. |
ISBN: | 9783642341656 3642341659 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-642-34166-3_78 |