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» A Multiple Hypothesis Approach to Figure Tracking
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CVPR
2004
IEEE
14 years 9 months ago
Gibbs Likelihoods for Bayesian Tracking
Bayesian methods for visual tracking model the likelihood of image measurements conditioned on a tracking hypothesis. Image measurements may, for example, correspond to various fi...
Stefan Roth, Leonid Sigal, Michael J. Black
WACV
2005
IEEE
14 years 28 days ago
Learning to Track Objects Through Unobserved Regions
As tracking systems become more effective at reliably tracking multiple objects over extended periods of time within single camera views and across overlapping camera views, incre...
Chris Stauffer
IJCV
2000
162views more  IJCV 2000»
13 years 7 months ago
Visual Surveillance for Moving Vehicles
An overview is given of a novel vision system for locating, recognising and tracking multiple vehicles, using a single monocular camera mounted on a moving vehicle1 . 3D model-bas...
James M. Ferryman, Stephen J. Maybank, Anthony D. ...
ACL
2009
13 years 5 months ago
Incremental HMM Alignment for MT System Combination
Inspired by the incremental TER alignment, we re-designed the Indirect HMM (IHMM) alignment, which is one of the best hypothesis alignment methods for conventional MT system combi...
Chi-Ho Li, Xiaodong He, Yupeng Liu, Ning Xi
TSP
2010
13 years 2 months ago
Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR
In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multi-target state estimat...
Daniel Clark, Branko Ristic, Ba-Ngu Vo, Ba-Tuong V...