Particle Probability-hypothesis-density Filter with Kernel Based State Extraction for Efficient Multi-target Visual Tracking
نویسندگان
چکیده
منابع مشابه
Multiple Target Tracking with The Probability Hypothesis Density Filter
The random-set framework for multiple target tracking offers a distinct alternative to the traditional approach to multiple target tracking by treating the collections of individual targets and observations as finite-sets. The multi-target state is predicted and updated recursively based on the set-valued observation. The complexity of computing the multi-target recursion grows exponentially wi...
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...
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: Information Technology Journal
سال: 2013
ISSN: 1812-5638
DOI: 10.3923/itj.2013.4176.4179