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.
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