Zhong Zhang
ABSTRACT
The goal of a fall detection system is to automatically detect cases where a human falls and may have been injured. A natural application of such a system is in home monitoring of patients and elderly persons, so as to automatically alert relatives and/or authorities in case of an injury caused by a fall. This paper describes experiments with three computer vision methods for fall detection in a simulated home environment. The first method makes a decision based on a single frame, simply based on the vertical position of the image centroid of the person. The second method makes a threshold-based decision based on the last few frames, by considering the number of frames during which the person has been falling, the magnitude (in pixels) of the fall, and the maximum velocity of the fall. The third method is a statistical method that makes a decision based on the same features as the previous two methods, but using probabilistic models as opposed to thresholds for making the decision. Preliminary experimental results are promising, with the statistical method attaining relatively high accuracy in detecting falls while at the same time producing a relatively small number of false positives.
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Index Terms
- Experiments with computer vision methods for fall detection
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