Segmenting foreground objects from a dynamic textured background via a robust Kalman filter

By Jing Zhong, Stan Sclaroff. Proceedings Ninth IEEE International Conference on Computer Vision, 2003

382 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/e4dcdfbd823231e7d168309983c07dc2a90b9f7c

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Citations in the corpus

Spatiotemporal Saliency in Dynamic Scenes (2010, methodology): “We adopt dynamic texture models, due to their versatility in modeling complex moving patterns and ability to replicate natural scene dynamics [11], [31].”
Real-time foreground-background segmentation using codebook model (2005, background): “1077-2014/$ - see front matter r 2005 Elsevier Ltd.”
Motion-based background subtraction using adaptive kernel density estimation (2004, methodology): “One can observe that even recent predictive methods do not model multiple modalities of dynamic behavior [20, 26, 23], and therefore such models can be quite inefficient in many practical scenarios.”
Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art (2008, background): “Although motion-based approaches allows predicting the motion pattern of each pixel, this approach might fail when there is no obvious difference between the motion fields of the foreground and background [58].”
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, methodology): “Similar idea was used by Zhong and Sclaroff [101] and Liu et.”
Background Subtraction for Freely Moving Cameras (2009, background): “[20], Zhong and Sclaroff [38], and Mittal and Paragios [19]).”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, methodology): “[425] used Kalman filter to detect bottles in waving river for an application of type (1).”
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation (2011, methodology): “Typical methods include single Gaussian distribution [27], Mixture of Gaussian [28], kernel density estimation [29], [30], block correlation [31], codebook model [32], Hidden Markov model [33], [34] and linear autoregressive models [8], [35], [36].”
Background-subtraction using contour-based fusion of thermal and visible imagery (2007, methodology): “Kalman filters were used in [55], and an auto-regressive model was used in [32].”
Bayesian modeling of dynamic scenes for object detection (2005, methodology): “The local search proposed in [7], the SDG of [29], the time series models of [25], [40], and KDEs over color and optical flow in [24] are several formulations proposed for detection nonstationary backgrounds .”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, background): “Other improvements concern the texture features instead of the intensity features [324][306][301], and a local-region feature rather than a pixel feature [98].”