SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity

619 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Recognition in Terra Incognita (2018, background): “background in video is a well studied problem [36], [37].”
Deep background subtraction with scene-specific convolutional neural networks (2016, methodology): “authors have worked on other components such as postprocessing operations [6] or feedback loops to update model parameters ([3], [7]).”
A Deep Convolutional Neural Network for Background Subtraction (2017, methodology): “Here we combine the segmentation mask from SuBSENSE algorithm [21] and the output of Flux Tensor algorithm [26], which can dynamically change the parameters used in the background model based on the motion changes in the video frames.”
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery (2017, methodology): “We selected three top performing unsupervised methods called SuBSENSE [76], PAWCS [75], and LOBSTER [77].”
Rain Removal in Traffic Surveillance: Does it Matter? (2018, methodology): “The concrete effects on a state-of-the-art unsupervised segmentation algorithm [11] is shown in Figure 3.”