Abstract
Vision-based fall detection is a challenging problem in pattern recognition. This paper introduces an approach to detect a fall as well as its type in infrared video sequences. The regions of interest of the segmented humans are examined image by image though calculating geometrical and kinematic features. The human fall pattern recognition system identifies true and false falls. The fall indicators used as well as their fuzzy model are explained in detail. The fuzzy model has been tested for a wide number of static and dynamic falls.
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Benocci, M., Tacconi, C., Farella, E., Benini, L., Chiari, L., Vanzago, L.: Accelerometer-based fall detection using optimized ZigBee data streaming. Microelectronics Journal 41(11), 703–710 (2010)
Doukas, C., Maglogiannis, I.: Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components. IEEE Transactions on Information Technology in Biomedicine 15(2), 277–289 (2011)
Fernández-Caballero, A., Castillo, J.C., Rodríguez-Sánchez, J.M.: Human activity monitoring by local and global finite state machines. Expert Systems with Applications 39(8), 6982–6993 (2012)
Fernández-Caballero, A., Castillo, J.C., Serrano-Cuerda, J., Maldonado-Bascón, S.: Real-time human segmentation in infrared videos. Expert Systems with Applications 38(3), 2577–2584 (2011)
Fernández-Caballero, A., Castillo, J.C., Martínez-Cantos, J., Martínez-Tomás, R.: Optical flow or image subtraction in human detection from infrared camera on mobile robot. Robotics and Autonomous Systems 58(12), 1273–1283 (2010)
Khawandi, S., Daya, B., Chauvet, P.: Implementation of a monitoring system for fall detection in elderly healthcare. Procedia Computer Science 3, 216–220 (2011)
Klack, L., Möllering, C., Ziefle, M., Schmitz-Rode, T.: Future care floor: A sensitive floor for movement monitoring and fall detection in home environments. In: Proceedings of MobiHealth 2010, pp. 211–218 (2010)
Litvak, D., Zigel, Y., Gannot, I.: Fall detection of elderly through floor vibrations and sound. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4632–4635 (2008)
Loomis, A.: Figure Drawing for All it’s Worth. Viking Adult (1943)
Mo, H.C., Leou, J.J., Lin, C.S.: Human behavior analysis using multiple 2d features and multi category support vector machine. In: Proceedings of the IAPR Conference on Machine Vision Applications, pp. 46–49 (2009)
Moreno-Garcia, J., Rodriguez-Benitez, L., Fernández-Caballero, A., López, M.T.: Video sequence motion tracking by fuzzification techniques. Applied Soft Computing 10(1), 318–331 (2010)
Pellegrini, S., Iocchi, L.: Human posture tracking and classification through stereo vision and 3d model matching. EURASIP Journal on Image and Video Processing, 1–12 (2008)
Rimminen, H., Lindström, J., Linnavuo, M., Sepponen, R.: Detection of falls among the elderly by a floor sensor using the electric near field. IEEE Transactions on Information Technology in Biomedicine 14(6), 1475–1476 (2010)
Rojas-Albarracín, G., Carbajal, C.A., Fernández-Caballero, A., López, M.T.: Skeleton simplfication by key points identfication. In: Proceedings of the 2nd Mexican Conference on Pattern Recognition, pp. 30–39 (2010)
Thome, N., Miguet, S., Ambellouis, S.: A real-time, multiview fall detection system: A LHMM-based approach. IEEE Transactions on In Circuits and Systems for Video Technology 18(11), 1522–1532 (2008)
Wei, X., Chai, J.: Modeling 3d human poses from uncalibrated monocular images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1873–1880 (2009)
Yu, X.: Approaches and principles of fall detection for elderly and patient. In: Proceedings of the 10th IEEE HealthCom 2008, pp. 42–47 (2008)
Zweng, A., Zambanini, S., Kampel, M.: Introducing a Statistical Behavior Model into Camera-Based Fall Detection. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 163–172. Springer, Heidelberg (2010)
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Sokolova, M.V., Fernández-Caballero, A. (2012). Fuzzy Sets for Human Fall Pattern Recognition. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera López, J.A., Boyer, K.L. (eds) Pattern Recognition. MCPR 2012. Lecture Notes in Computer Science, vol 7329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31149-9_12
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DOI: https://doi.org/10.1007/978-3-642-31149-9_12
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