Monocular Camera Fall Detection System Exploiting 3D Measures: A Semi-supervised Learning Approach

Konstantinos Makantasis¹, Eftychios Protopapadakis¹, Anastasios Doulamis¹, Lazaros Grammatikopoulos², and Christos Stentoumis³

¹Technical University of Crete, 73100, Chania, Greece
eft.protopapadakis@gmail.com
adoulam@cs.ntua.gr

²Technological Educational Institute of Athens, 12210, Athens, Greece
lazaros.pcvg@gmail.com

³National Technical University of Athens, 15773, Athens, Greece
cstent@mail.ntua.gr

Abstract. Falls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. The system presented in this article addresses the fall detection problem through visual cues. The proposed methodology utilize a fast, real-time background subtraction algorithm based on motion information in the scene and capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object and, at the same time, it exploits 3D space’s measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.

Keywords: image motion analysis, semisupervised learning, self calibration, fall detection

LNCS 7585, p. 81 ff.

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