Szczegóły publikacji

Opis bibliograficzny

Autorzy (2)

• Kępski Michał
• AGHKwolek Bogdan

Słowa kluczowe

Dane bibliometryczne

ID BaDAP	95146
Data dodania do BaDAP	2016-02-03
DOI	10.1109/IDAACS.2015.7341404
Rok publikacji	2015
Typ publikacji	materiały konferencyjne (aut.)
Otwarty dostęp
Konferencja	2015 IEEE 8th international conference on Intelligent Data Acquisition and Advanced Computing Systems: technology and applications

Abstract

This paper presents an embedded system for fall detection using accelerometric data and depth maps. A real-time processing of motion data and depth maps is realized on a low-cost PandaBoard platform. In order to achieve detection of human falls with low computational cost the system performs a depth-based inferring about the fall event when person's movement is above some preset threshold. The performance of the system has been evaluated on our publicly available dataset consisting of synchronized depth maps and motion data. To investigate the detection accuracy in depth maps from different camera views the image sequences were simultaneously recorded by two Kinect sensors, where one of them was placed in the front of the scene, whereas the second one was located on the ceiling. The motion data were acquired by a body-worn accelerometer and transmitted wirelessly to the processing unit, responsible for both synchronization and recording or processing of the data.

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