2010 · 13 citations · 8 references
ComputingComputer ScienceComputer VisionMachine VisionSpatial StatisticsBayesian FilteringGaussian ProcessesParticle FilteringPassive SurveillanceSignal Detection TheoryVisual TrackingGaussian Mixture ModelsMulti-object TrackingMulti-target TrackingMultitarget TrackingPassive Data CollectionTarget Tracking
This paper considers the challenging problem of multitarget tracking with passive data, obtained here by geographically distributed cameras. We use a Gaussian Mixture Probability Hypothesis Density filter approach to solve this difficult problem. As we make no spatial assumptions for the birth process, we use a slightly modified filter to obtain our results. We first describe the modified filter and detail our application before we present some results obtained on a realistic test scenario. Our simulations demonstrate that the proposed Probability Hypothesis Density filter is a promising candidate for three dimensional passive multi target tracking in clutter.
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The unscented Kalman filter for nonlinear estimation
Eric A. Wan, Rudolph van der Merwe · 2002 · 3.9K citations