40 search results - page 6 / 8 » Gibbs Likelihoods for Bayesian Tracking |
194
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EMMCVPR
2011
Springer
13 years 9 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 ...
IROS
2008
IEEE
15 years 4 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 ...
RSS
2007
14 years 11 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...
ICIP
2007
IEEE
15 years 11 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...
107
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ECCV
2000
Springer
15 years 12 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...