In recent years a growing interest on action recognition is observed, including detection of fall accident for the elderly. However, despite many efforts undertaken, the existing technology is not widely used by elderly, mainly because of its flaws like low precision, large number of false alarms, inadequate privacy preserving during data acquisition and processing. This research work meets these expectations. The work is empirical and it is situated in the field of computer vision systems. The main part of the work situates itself in the area of action and behavior recognition. Efficient algorithms for fall detection were developed, tested and implemented using image sequences and wireless inertial sensor worn by a monitored person. A set of descriptors for depth maps has been elaborated to permit classification of pose as well as the action of a person. Experimental research was carried out based on the prepared data repository consisting of synchronized depth and accelerometric data. The study was carried out in the scenario with a static camera facing the scene and an active camera observing the scene from above. The experimental results showed that the developed algorithms for fall detection have high sensitivity and specificity. The algorithm were designed with regard to low computational demands and possibility to run on ARM platforms. Several experiments including person detection, tracking and fall detection in real-time were carried out to show efficiency and reliability of the proposed solutions.