Abstract
Vision-based fall detection is a challenging problem in pattern recognition. This paper introduces an approach to detect a fall as well as its type in infrared video sequences. The regions of interest of the segmented humans are examined image by image though calculating geometrical and kinematic features. The human fall pattern recognition system identifies true and false falls. The fall indicators used as well as their fuzzy model are explained in detail. The fuzzy model has been tested for a wide number of static and dynamic falls. © 2012 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Sokolova, M. V., & Fernández-Caballero, A. (2012). Fuzzy sets for human fall pattern recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7329 LNCS, pp. 117–126). https://doi.org/10.1007/978-3-642-31149-9_12
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