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» An Adaptive Bayesian Technique for Tracking Multiple Objects
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CVPR
1998
IEEE
16 years 7 months ago
Joint Probabilistic Techniques for Tracking Multi-Part Objects
Common objects such as people and cars comprise many visual parts and attributes, yet image-based tracking algorithms are often keyed to only one of a target's identifying ch...
Christopher Rasmussen, Gregory D. Hager
ICCV
2009
IEEE
15 years 2 months ago
Tracking a large number of objects from multiple views
We propose a multi-object multi-camera framework for tracking large numbers of tightly-spaced objects that rapidly move in three dimensions. We formulate the problem of finding co...
Zheng Wu, Nickolay I. Hristov, Tyson L. Hedrick, T...
170
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ICCV
2001
IEEE
16 years 7 months ago
Continuous Global Evidence-Based Bayesian Modality Fusion for Simultaneous Tracking of Multiple Objects
Robust, real-time tracking of objects from visual data requires probabilistic fusion of multiple visual cues. Previous approaches have either been ad hoc or relied on a Bayesian n...
Jamie Sherrah, Shaogang Gong
ICCV
2009
IEEE
16 years 10 months ago
Adaptive Fragments-Based Tracking of Non-Rigid Objects Using Level Sets
We present an approach to visual tracking based on dividing a target into multiple regions, or fragments. The target is represented by a Gaussian mixture model in a joint feature...
Prakash Chockalingam, Nalin Pradeep
PAMI
2007
191views more  PAMI 2007»
15 years 4 months ago
Adaptive Object Tracking Based on an Effective Appearance Filter
We propose a similarity measure based on a Spatial-color Mixture of Gaussians (SMOG) appearance model for particle filters. This improves on the popular similarity measure based o...
Hanzi Wang, David Suter, Konrad Schindler, Chunhua...