Abstract
Falls are one of the major risks for seniors living alone at home. Computer vision systems, which do not require to wear sensors, offer a new and promising solution for fall detection. In this work, an occlusion robust method is presented based on two features: human centroid height relative to the ground and body velocity. Indeed, the first feature is an efficient solution to detect falls as the vast majority of falls ends on the ground or near the ground. However, this method can fail if the end of the fall is completely occluded behind furniture. Fortunately, these cases can be managed by using the 3D person velocity computed just before the occlusion.
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Rougier, C., Auvinet, E., Rousseau, J., Mignotte, M., Meunier, J. (2011). Fall Detection from Depth Map Video Sequences. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds) Toward Useful Services for Elderly and People with Disabilities. ICOST 2011. Lecture Notes in Computer Science, vol 6719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21535-3_16
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DOI: https://doi.org/10.1007/978-3-642-21535-3_16
Publisher Name: Springer, Berlin, Heidelberg
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