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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.

A Novel Shadow-Assistant Human Fall Detection Scheme Using a Cascade of SVM Classifiers