Abstract
Monitoring and tracking of elderly people using vision algorithms is an strategy gaining relevance to detect anomalous and potentially dangerous situations and react immediately. In general vision algorithms for monitoring and tracking are very costly and take a lot of time to respond, which is highly inconvenient since many applications can require action to be taken in real time. A multi-agent system (MAS) can establish a social model to automate the tasks carried out by the human experts during the process of analyzing images obtained by cameras. This study presents a detector agent integrated in a MAS that can process stereoscopic images to detect and classify situations and states of elderly people in geriatric residences by combining a series of novel techniques. We will talk in details about the combination of techniques used to perform the detection process, subdivided into human detection, human tracking ,and human behavior understanding, and where there is a case-based reasoning (CBR) model that allows the system to add reasoning capabilities.
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Reig-Bolaño, R., Marti-Puig, P., Bajo, J., Rodríguez, S., De Paz, J.F., Rubio, M.P. (2011). Image Processing to Detect and Classify Situations and States of Elderly People. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_18
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DOI: https://doi.org/10.1007/978-3-642-19644-7_18
Publisher Name: Springer, Berlin, Heidelberg
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