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» Kernel-Based Bayesian Filtering for Object Tracking
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CVPR
2005
IEEE
9 years 10 months ago
Kernel-Based Bayesian Filtering for Object Tracking
Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, the algorithm is based on a Monte Ca...
Bohyung Han, Ying Zhu, Dorin Comaniciu, Larry S. D...
CVPR
2004
IEEE
9 years 10 months ago
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
ICCV
2007
IEEE
8 years 10 months ago
Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering
Even though sensor fusion techniques based on particle ļ¬lters have been applied to object tracking, their implementations have been limited to combining measurements from multip...
Bohyung Han, Seong-Wook Joo, Larry S. Davis
ICIP
2006
IEEE
9 years 10 months ago
Robust Kernel-Based Tracking using Optimal Control
Although more efficient in computation compared to other tracking approaches such as particle filtering, the kernel-based tracking suffers from the "singularity" problem...
Wei Qu, Dan Schonfeld
ICIP
2005
IEEE
9 years 10 months ago
Bayesian visual tracking with existence process
Most object tracking approaches either assume that the number of objects is constant, or that information about object existence is provided by some external source. Here, we show...
Jaco Vermaak, Mark Briers, Patrick Pérez, S...
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