Lifestyle, Americas, Special Report

[ms-marvelnen]

Scene Background Initialization (SBI) remains an important task in the field of scene background modeling, and it has a wide range of applications, such as video surveillance, video segmentation, video compression, video inpainting, privacy protection for videos, computation photography, and so on. In real scenes, there exist challenges to achieve this aim such as sudden illumination changes, night videos, low framerate, dynamic background, camera jitter, and so on. In order to address these challenges, various background modeling methods have been proposed over the last few decades. Background modeling approaches based on deep learning have been developed in recent years. Random Access Memory (RAM) based neural network method to identify sections of the background scene in each test image. However, this approach requires the background of the scene to be correctly represented by the images, and there is no background maintenance stage. Self Organizing Background Subtraction (SOBS) which learns background motion trajectories in a self organizing manner. CNN-based approach to obtain the initial background of a scene by using just a small set of frames containing foreground objects.




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