40 search results - page 6 / 8 » Gibbs Likelihoods for Bayesian Tracking |
327
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EMMCVPR
2011
Springer
14 years 3 months ago
2011
Springer
Abstract. We present two data-driven importance distributions for particle filterbased articulated tracking; one based on background subtraction, another on depth information. In ...
118
Voted
IROS
2008
IEEE
15 years 10 months ago
2008
IEEE
— In order to be truly autonomous, robots that operate in natural, populated environments must have the ability to create a model of these unpredictable dynamic environments and ...
131
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RSS
2007
15 years 4 months ago
2007
— In probabilistic mobile robotics, the development of measurement models plays a crucial role as it directly influences the efficiency and the robustness of the robot’s perf...
128
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ICIP
2007
IEEE
16 years 5 months ago
2007
IEEE
The Bayesian filtering for recursive state estimation and the shape-based matching methods are two of the most commonly used approaches for target tracking. The Multiple Hypothesi...
156
Voted
ECCV
2000
Springer
16 years 5 months ago
2000
Springer
A probabilistic method for tracking 3D articulated human figures in monocular image sequences is presented. Within a Bayesian framework, we define a generative model of image appea...