Abstract
In order to develop a stable algorithm for automatic detection and tracking of nondeterministicobjects with real-time learning for embedded computing systems with optoelectronicdevices, within the framework of this work, a study and analysis of the existing world scientific andtechnical experience in the field of automatic tracking algorithms for general purposes was carriedout. The article shows that the most stable modern automatic tracking algorithms are a systemthat makes a decision about the current position, size and other parameters of the trackedimage based on the model being trained. The authors of the study identified the most effective ofthe 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-deterministicobjects. A semi-natural testing of the developed algorithm was carried out and its effectivenesswas evaluated in solving problems not only of automatic tracking of objects, but also problems ofautomatic detection of objects using several reference images. In conclusion, proposals are presentedfor further improving the accuracy of the developed algorithm and for its optimization andimplementation in the special software of on-board computer systems of aircraft.
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