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Found 3 papers, 1 papers with code
Weakly Supervised Realtime Dynamic Background Subtraction
In this work, we propose a weakly supervised framework that can perform background subtraction without requiring per-pixel ground-truth labels.
Dynamic Background Subtraction by Generative Neural Networks
One of the challenges for background subtraction methods is dynamic background, which constitute stochastic movements in some parts of the background.
Online Illumination Invariant Moving Object Detection by Generative Neural Network
In this paper, we propose an extension of a state-of-the-art batch MOD method (ILISD) to an online/incremental MOD using unsupervised and generative neural networks, which use illumination invariant image representations.
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