Abstract
The research is devoted to the analysis and improvement of video analytics functions in video surveillance systems in order to increase the efficiency of detection of dynamic objects in the sectors of video surveillance. It has been established that video analytics methods using background subtraction and object recognition methods have significant disadvantages, namely: algorithms cannot select an object from the background at low contrast; some moving objects can be recognized as backgrounds; algorithms critically depend on lighting conditions, etc. Thus, the aim of the study is to improve the method of detecting dynamic objects in video sequences, which uses methods of subtraction of the background, based on pixel analysis of frames using elements of the theory of expert systems. The advanced method of detecting dynamic objects in video sequences is based on the ViBe algorithm, and differs from the original in that it uses a color model U * V * W * with double threshold levels and expert systems to eliminate uncertainties in the classification of pixels (Dempster-Schaefer theory) and the dynamic method of updating background pixel models.
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