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» Novel Observation Model for Probabilistic Object Tracking
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CVPR
2008
IEEE
14 years 9 months ago
Real-time pose estimation of articulated objects using low-level motion
We present a method that is capable of tracking and estimating pose of articulated objects in real-time. This is achieved by using a bottom-up approach to detect instances of the ...
Ben Daubney, David P. Gibson, Neill W. Campbell
TCSV
2008
155views more  TCSV 2008»
13 years 7 months ago
Heterogeneous Fusion of Omnidirectional and PTZ Cameras for Multiple Object Tracking
Dual-camera systems have been widely used in surveillance because of the ability to explore the wide field of view (FOV) of the omnidirectional camera and the wide zoom range of th...
Chung-Hao Chen, Yi Yao, David Page, Besma R. Abidi...
ICCV
2007
IEEE
14 years 9 months ago
Learning Higher-order Transition Models in Medium-scale Camera Networks
We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the...
Ryan Farrell, David S. Doermann, Larry S. Davis
ICIP
2005
IEEE
14 years 9 months ago
Joint feature-spatial-measure space: a new approach to highly efficient probabilistic object tracking
In this paper we present a probabilistic framework for tracking objects based on local dynamic segmentation. We view the segn to be a Markov labeling process and abstract it as a ...
Feng Chen, XiaoTong Yuan, ShuTang Yang
IVC
2006
154views more  IVC 2006»
13 years 7 months ago
Manifold based analysis of facial expression
We propose a novel approach for modeling, tracking and recognizing facial expressions. Our method works on a low dimensional expression manifold, which is obtained by Isomap embed...
Ya Chang, Changbo Hu, Rogerio Feris, Matthew Turk