ANALYSIS OF INFORMATION TECHNOLOGIES FOR REMOTE IDENTIFICATION OF DYNAMIC OBJECTS
DOI:
https://doi.org/10.20998/2079-0023.2023.01.17Keywords:
Remote identification of dynamic objects, object detection, optical flow, velocity identification, deep learning, convolutional neural networksAbstract
The problem of identification of dynamic objects using remote identification information technologies is considered. It is noted that the identification of moving objects is important in various fields, including autonomous vehicles, medical diagnostics and robotics. The purpose of the article is to analyze various information technologies for detecting objects that can be used in future research on remote identification. Analysis of methods for determining speed as a dynamic parameter, analysis of two-step and one-step methods of remote identification of objects, analysis of early identification methods, as well as analysis of methods for improving remote identification of objects was carried out. Several means of determining the motion of objects are considered, in particular, the proportional-integral-differential controller, the leveling block method, phase correlation, pixel recursion algorithms, and the optical flow methods of Lucas – Kanady, Horn – Shunk, Farnbeck, dense optical flow. These tools can be used to effectively determine the movement of objects and identify their speed regardless of the size and position of the objects. Two-step and one-step object detection methods are considered: region method with convolutional neural networks, its improvements, spatial pyramid pooling networks, "You only look once" method, one-step multi-frame method, retinal networks, corner network, central network and detection transformer, which use different approaches to improve the performance and accuracy of object detection. The necessity of using methods of convolutional neural networks and spatial pyramid pooling networks for effective identification of objects regardless of their size and position is emphasized. New approaches are proposed that allow creating fixed-length representations for image processing and regions of interest, as well as Viola – Jones methods, oriented gradient histograms, and deformed part models. Research in the field of object detection contributes to the development of information technologies and the improvement of the efficiency of dynamic object identification systems. Through the review and analysis of various methods, recommendations for researchers and practitioners working in the field of remote identification of dynamic objects are provided.
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