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Activity Recognition from Video in Day or Night Using Fuzzy Clustering Techniques

T.Udhayakumar, S.Anandha Saravanan

An approach for activity state recognition implemented on data collected from various sensors – standard web cameras under normal illumination, web cameras using infrared lighting, and the inexpensive Microsoft Kinect camera system. Sensors such as the Kinect ensure that activity segmentation is possible during day time as well as night. It is especially useful for activity monitoring of older adults since falls are more prevalent at night than during the day. The project is an application of fuzzy set techniques to a new domain. The approach described herein is capable of accurately detecting several different activity states related to fall detection and fall risk assessment including sitting, being upright, and being on the floor to ensure that elderly residents get the help they need quickly in case of emergencies and ultimately to help prevent such emergencies. All detection and fall risk assessment are major goals of research and continue to conduct experiments, in particular, an aging in place facility for the elderly. It describes the silhouette extraction process, the image features employed, and the fuzzy clustering technique used in the work.