An analytical review\ of architectures, models, methods and algorithms\ for localization and tracking of non-rigid objects

Автор: Gricenko G.G., Fralenko V.P., Khachumov V.M., Znamenskij S.V.

Журнал: Программные системы: теория и приложения @programmnye-sistemy

Рубрика: Искусственный интеллект и машинное обучение

Статья в выпуске: 4 (63) т.15, 2024 года.

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Computer vision requires video stream analysis, including extracting information from frames, detecting specific objects, and collecting data about them. After detection, tracking or following objects in the video stream is often required. Non-rigidity or shape variability hinders object analysis, complicates their detection and tracking, and worsens localization. The review considers architectures, models, methods, and algorithms used in practice for detection and tracking of non-rigid objects, and highlights promising solutions.

Non-rigid object, artificial neural network, deep learning, object localization, object tracking, fire and smoke detection, medical image analysis

Короткий адрес: https://sciup.org/143183787

IDR: 143183787   |   DOI: 10.25209/2079-3316-2024-15-4-111-151

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