Abstract
Social progress and demographic changes favor increased life expectancy and the number of people in situations of dependency. As a consequence, the demand for support systems for personal autonomy is increasing. This article outlines the vision @ home project, whose goal is the development of vision-based services for monitoring and recognition of the activity carried out by individuals in the home. Incorporating vision devices in private settings is justified by its power to extract large amounts of data with low cost but must safeguard the privacy of individuals. The vision system we have designed incorporates a knowledge base containing information from the environment, parameters of different cameras used, human behavior modeling and recognition, and information about people and objects. By analyzing the scene, we infer its context, and provide a privacy filter which is able to return textual information, as well as synthetic and real images.
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References
Poppe, R.: A survey on vision-based human action recognition. Image Vision Comput. 28(6), 976–990 (2010)
Bobick, A.F., Davis, J.W.: The Recognition of Human Movement Using Temporal Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(3), 257–267 (2001)
Oikonomopoulos, A., Patras, I., Pantic, M.: Spatiotemporal salient points for visual recognition of human actions. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics: A Publication of the IEEE Systems, Man, and Cybernetics Society 36(3), 710–719 (2006)
Chen, L., Wei, H., Ferryman, J.M.: A Survey of Human Motion Analysis using Depth Imagery. Pattern Recognition Letters (February 2013)
Chen, D., Chang, Y., Yan, R., Yang, J.: Protecting personal identification in video. In: Senior, A. (ed.) Protecting Privacy in Video Surveillance, pp. 115–128. Springer, London (2009)
Chaaraoui, A.A., Climent-Pérez, P., Flórez-Revuelta, F.: A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living. Expert Systems with Applications 39(12), 10873–10888 (2012)
Chaaraoui, A.A., Climent-Pérez, P., Flórez-Revuelta, F.: Silhouette-based Human Action Recognition using Sequences of Key Poses. Pattern Recognition Letters 34(15), 1799–1807 (2013), http://dx.doi.org/10.1016/j.patrec.2013.01.021
Chaaraoui, A.A., Climent-Pérez, P., Flórez-Revuelta, F.: An Efficient Approach for Multi-view Human Action Recognition Based on Bag-of-Key-Poses. In: Salah, A.A., Ruiz-del-Solar, J., Meriçli, Ç., Oudeyer, P.-Y. (eds.) HBU 2012. LNCS, vol. 7559, pp. 29–40. Springer, Heidelberg (2012)
Chaaraoui, A.A., Flórez-Revuelta, F.: Human Action Recognition Optimization Based on Evolutionary Feature Subset Selection. In: GECCO 2013: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (to appear, 2013)
Climent-Pérez, P., Chaaraoui, A., Padilla-López, J., Flórez-Revuelta, F.: Evolutionary joint selection to improve human action recognition with RGB-D devices. Expert Systems with Applications (2013) ISSN 0957-4174, http://dx.doi.org/10.1016/j.eswa.2013.08.009
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Chaaraoui, A.A. et al. (2013). A Vision System for Intelligent Monitoring of Activities of Daily Living at Home. In: Nugent, C., Coronato, A., Bravo, J. (eds) Ambient Assisted Living and Active Aging. IWAAL 2013. Lecture Notes in Computer Science, vol 8277. Springer, Cham. https://doi.org/10.1007/978-3-319-03092-0_14
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DOI: https://doi.org/10.1007/978-3-319-03092-0_14
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