AN IMPROVED MOVING OBJECTS DETECTION ALGORITHM IN VIDEO SEQUENCES - Katerynchuk, I. S.; Babaryka, A. O. (Radio Electronics, Computer Science, Control, 2020)
Context. The implementation of video analytics functions in video surveillance systems makes it possible to increase the efficiency of these systems. One of the functions of these intelligent video surveillance systems is to detect dynamic objects in the surveillance sectors of video surveillance cameras. Existing methods of background subtractoin and object recognition have important disadvantages that limit their application in practice: under low contrast algorithms can not select an object from the background; some moving objects can be recognized as a background, algorithms critical to lighting conditions, and so on. Therefore, an important task is to develop and improve methods for detecting dynamic objects in video sequences. Objective. The research is devoted to the development of an improved method for detecting dynamic objects in video sequences. Method. For moving objects detection in video sequences we used background subtraction methods based on pixel-by-pixel analysis of frames using elements of the expert systems theory. Results. In this paper, we propose an improved method for detecting dynamic objects in video sequences, which is based on the ViBe algorithm. The proposed approach differs from original the using of U*V*W* color model, using double threshold levels and some elements of theory expert systems for removal of vaguenesses in pixel classification (Dempster-Shafer theory) and dynamic method for updating background pixel models. Proposed algorithm include following stages: initialization of the background model (for each pixel with known parameters, the number of previous values in the current frame is stored); foreground detection; the next step is a calculation amounts of points, that belong to the foreground and to the background. For removal of vaguenesses in pixel classification we used some elements of Dempster-Shafer theory. After initialization of the background model and foreground detection next stage is updating background model. For this we used a three-level constructed neighborhood of the studied pixel and used of the even distribution of random values is into each of three levels. Conclusions. Experimental research of the improved algorithm in comparing to original ViBe conducted with the use of test frames from a set of CDNET in the various variants of environment and with the different variants of discriminability. The consolidated results specify on the improvement of results of an offer method as compared to original ViBe on the average on 6,7%.