Abstract
Automatic fall detection using computer vision is a particular case for real time video analysis, efficient in kindergartens. This paper is focused on the design and implementation of a Human activity analysis system. The multiple cameras sends captured frames to the monitoring system via the local network. Through the use of human silhouette, acquired from a smart camera, a shape representation of the human beings was built in real-time and a fuzzy logic inference system was developed for fall detection. The system also allows tracking and localizing children within an authorized area. The alarm is triggered in case of transgression. Experimental results prove that the fuzzy inference system is efficient.
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Acknowledgments
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program. The authors are grateful to the owner and staff of the ’King Kid’ kindergarten in Sfax, Tunisia for allowing access to the institution, and for their help in the implementation and test of the designed surveillance system.
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Abdelhedi, S., Wali, A., Alimi, A.M. (2016). Fuzzy Logic Based Human Activity Recognition in Video Surveillance Applications. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_23
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DOI: https://doi.org/10.1007/978-3-319-29504-6_23
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