Abstract:
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.
Key words and phrases:non-rigid object, artificial neural network, deep learning, object localization, object tracking, fire and smoke detection, medical image analysis.