Particle probability hypothesis density filtering for multitarget visual tracking with robust state extraction
نویسندگان
چکیده
منابع مشابه
A shrinkage probability hypothesis density filter for multitarget tracking
In radar systems, tracking targets in low signal-to-noise ratio (SNR) environments is a very important task. There are some algorithms designed for multitarget tracking. Their performances, however, are not satisfactory in low SNR environments. Track-before-detect (TBD) algorithms have been developed as a class of improved methods for tracking in low SNR environments. However, multitarget TBD i...
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This paper addresses the problem of tracking multiple moving targets by recursively estimating the joint multitarget probability density (JMPD). Estimation of the JMPD is done in a Bayesian framework, providing a method of tracking multiple targets which allows nonlinear target motion, nonlinear measurement to state coupling, and non-Gaussian target state densities. We utilize a particle filter...
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When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can instead be tracked with a separate filter. However, this leads to a model-data association problem. Another approach to solve the problem with computational complexity is to track only the first...
متن کاملMulti-target particle filtering for the probability hypothesis density
When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can instead be tracked with a separate filter. However, this leads to a model-data association problem. Another approach to solve the problem with computational complexity is to track only the first...
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ژورنال
عنوان ژورنال: Optical Engineering
سال: 2011
ISSN: 0091-3286
DOI: 10.1117/1.3638121