@article{
author = "Gnjatović, Milan and Maček, Nemanja and Saračević, Muzafer and Adamović, Saša and Joksimović, Dušan and Karabašević, Darjan",
year = "2023",
abstract = "This paper introduces a heuristic for multiple sequence alignment aimed at improving
real-time object recognition in short video streams with uncertainties. It builds upon the idea of
the progressive alignment but is cognitively economical to the extent that the underlying edit dis-
tance approach is adapted to account for human working memory limitations. Thus, the proposed
heuristic procedure has a reduced computational complexity compared to optimal multiple sequence
alignment. On the other hand, its relevance was experimentally confirmed. An extrinsic evaluation
conducted in real-life settings demonstrated a significant improvement in number recognition accu-
racy in short video streams under uncertainties caused by noise and incompleteness. The second line
of evaluation demonstrated that the proposed heuristic outperforms humans in the post-processing
of recognition hypotheses. This indicates that it may be combined with state-of-the-art machine
learning approaches, which are typically not tailored to the task of object sequence recognition from a
limited number of frames of incomplete data recorded in a dynamic scene situation",
publisher = "Basel, Switzerland : MDPI",
journal = "Axioms",
title = "Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties",
volume = "12",
number = "1",
pages = "3",
doi = "10.3390/axioms12010003"
}