Motion-based background subtraction using adaptive kernel density estimation

By Anurag Mittal, Nikos Paragios. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004

738 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5e527a918dbcc4874814cfb6cb89c99ac95f98c1

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Citations in the corpus

Comparative study of background subtraction algorithms (2010, methodology): “More sophisticated methods can also be envisaged such as Mittal and Paragios’s [26] which is based on “Variable Bandwidth Kernels”.”
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation (2011, methodology): “Typical me thods include single Gaussian distribution [27], Mixture of Gaussian [28], kernel densit y estimation [29], [30], block correlation [31], codebook model [32], Hidden Markov model [33], [34] an d linear autoregressive models [8], [35], [36].”
Bayesian modeling of dynamic scenes for object detection (2005, background): “" Nonstationary " backgrounds have most recently been addressed by Pless et al. [28] and Mittal and Paragios [24].”
Background Subtraction for Freely Moving Cameras (2009, background): “[20], Zhong and Sclaroff [38], and Mittal and Paragios [19]).”
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, methodology): “Optical flow [12], [19] Computes motion maps or flow vectors to determine consistent motion patterns Cluster using modified kmeans clustering on flow maps or compute flow equation at each pixel Wakes and waves are automatically suppressed due to inconsistent motion patterns Cannot deal with boat man”
Background-subtraction using contour-based fusion of thermal and visible imagery (2007, background): “A variable-bandwidth kernel density estimator was proposed in [31].”
Real-time foreground-background segmentation using codebook model (2005, methodology): “Methods employing MOG have been widely incorporated into algorithms that utilize Bayesian frameworks [4], dense depth data [5], color and gradient information [6], mean-shift analysis [7], and region-based information [8].”
SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity (2015, methodology): “Nonparametric models based on Kernel Density Estimation (KDE) [14] were also introduced early on and improved in more recent state-of-the-art methods [11], [15], [16].”
Efficient adaptive density estimation per image pixel for the task of background subtraction (2006, methodology): “In (Mittal and Paragios, 2004) an elaborate hybrid scheme is used.”
Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art (2008, background): “According to Mittal and Paragios [65], existing methods for background adaptation may be classified as either predictive or non-predictive.”
Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey (2011, methodology): “For the feature type, several featur s are used instead of the RGB space like the edge feature s [156] and motion features [157].”