DOI: https://doi.org/10.15588/1607-3274-2018-4-18

ADAPTIVE MATRIX MODELS IN THE VIDEO STREAMS CONTROL PROBLEM

S.V. Mashtalir, M.I. Stolbovyi

Abstract


Context. At present, the multidimensional data analysis is one of the priority scientific research areas. This is due to the almost
uncontrollable growth in the information size and the need to obtain/search for various kinds of useful data from it. At the same time,
video data analysis is one of the most difficult from a computational point of view, not only because of BigData being processed, but
also due to the video unstructuredness, and also the fact that in a bunch of video processing applications exist limitations on the
processing time. One of the ways to solve these video analysis problems is to pre-process the initial data in order to get them split
into homogeneous segments (shots), which significantly reduces the time and computational costs for further content-based video
analysis in video database. And, despite the existing results in this direction, the video sequences clustering/segmentation problem
remains extremely relevant.
Objective. The paper considers the problem of clustering multidimensional streaming data as example of temporal video
segmentation.
Method. A method for controlling changes in streaming data is proposed, which allows you to detect the moments of a
significant change in the input multidimensional data characteristics, based on adaptive matrix models with the specialized tuning
algorithm for the predictive model introduction.
Results. The conducted experiment on an arbitrary video sequences demonstrated the video shot detection possibility. It should
be noted that the proposed approach essentially depends on the input data spatial segmentation results, which is necessary to obtain a
characteristics set describing each frame of the video sequence.
Conclusions. The proposed method allows multidimensional input data clustering/segmentation by adaptive matrix models. As
initial data in the experimental part, video sequences were used.

Keywords


video data; clustering; adaptive matrix models; tuning criteria; tracking signal.

References


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