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» Non-parametric Model for Background Subtraction
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CRV
2011
IEEE
305views Robotics» more  CRV 2011»
12 years 7 months ago
Motion Segmentation by Learning Homography Matrices from Motor Signals
—Motion information is an important cue for a robot to separate foreground moving objects from the static background world. Based on the observation that the motion of the backgr...
Changhai Xu, Jingen Liu, Benjamin Kuipers
DAGM
2009
Springer
14 years 2 months ago
Localised Mixture Models in Region-Based Tracking
An important problem in many computer vision tasks is the separation of an object from its background. One common strategy is to estimate appearance models of the object and backgr...
Christian Schmaltz, Bodo Rosenhahn, Thomas Brox, J...
CVPR
2005
IEEE
14 years 9 months ago
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
We present a new method to robustly and efficiently analyze foreground when we detect background for a fixed camera view by using mixture of Gaussians models and multiple cues. Th...
Ying-li Tian, Max Lu, Arun Hampapur
ACCV
2007
Springer
14 years 1 months ago
Robust Foreground Extraction Technique Using Gaussian Family Model and Multiple Thresholds
Abstract. We propose a robust method to extract silhouettes of foreground objects from color video sequences. To cope with various changes in the background, the background is mode...
Hansung Kim, Ryuuki Sakamoto, Itaru Kitahara, Tomo...
FGR
2000
IEEE
163views Biometrics» more  FGR 2000»
14 years 4 days ago
Tracking Interacting People
A computer vision system for tracking multiple people in relatively unconstrained environments is described. Trackerformed at three levels of abstraction: regions, people and grou...
Stephen J. McKenna, Sumer Jabri, Zoran Duric, Harr...