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3D measures exploitation for a monocular semi-supervised fall detection system

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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. Thus, the development of robust home surveillance systems is of great importance. In this article, such a system is presented, which tries to address the fall detection problem through visual cues. The proposed methodology utilizes a fast, real-time background subtraction algorithm, based on motion information in the scene and pixels intensity, capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object. 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 approach. 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.

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Acknowledgments

The research leading to these results has been supported by European Union funds and national funds from Greece and Cyprus under the project POSEIDON: Development of an Intelligent System for Coast Monitoring using Camera Arrays and Sensor Networks in the context of the inter-regional programme INTERREG (Greece-Cyprus cooperation) - contract agreement K1 3 1017/6/2011. The work has, also, been supported by IKY Fellowships of excellence for post graduate studies in Greece-Siemens Program.

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Authors and Affiliations

  1. Technical University of Crete, Chania, Greece

    Konstantinos Makantasis, Eftychios Protopapadakis & Nikolaos Matsatsinis

  2. National Technical University of Athens, Athens, Greece

    Anastasios Doulamis & Nikolaos Doulamis

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  1. Konstantinos Makantasis
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  2. Eftychios Protopapadakis
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  3. Anastasios Doulamis
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  4. Nikolaos Doulamis
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  5. Nikolaos Matsatsinis
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Correspondence to Eftychios Protopapadakis.

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Makantasis, K., Protopapadakis, E., Doulamis, A. et al. 3D measures exploitation for a monocular semi-supervised fall detection system. Multimed Tools Appl 75, 15017–15049 (2016). https://doi.org/10.1007/s11042-015-2513-9

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  • DOI: https://doi.org/10.1007/s11042-015-2513-9

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