CORRELATIONAL SUPPORT ALGORITHM WITH REAL-TIME LEARNING

Abstract

In order to develop a stable algorithm for automatic detection and tracking of nondeterministic objects with real-time learning for embedded computing systems with optoelectronic devices, within the framework of this work, a study and analysis of the existing world scientific and technical experience in the field of automatic tracking algorithms for general purposes was carried out. The article shows that the most stable modern automatic tracking algorithms are a system that makes a decision about the current position, size and other parameters of the tracked image based on the model being trained. The authors of the study identified the most effective of the applied basic algorithms suitable for use in embedded computing systems of robotic complexes, and developed a new algorithm for automatic detection and maintenance of non-deterministic objects. A semi-natural testing of the developed algorithm was carried out and its effectiveness was evaluated in solving problems not only of automatic tracking of objects, but also problems of automatic detection of objects using several reference images. In conclusion, proposals are presented for further improving the accuracy of the developed algorithm and for its optimization and implementation in the special software of on-board computer systems of aircraft.