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This paper presents a particle filter for multiple target tracking. The main contribution of this work is in the proposed likelihood function accounting for the interactions between the objects. The filter likelihood function is calculated by combining a social force model for human behaviour with image features such as colour and motion. The added social force model contributes to coping with occlusions...
Global Navigation Satellite Systems (GNSS)-based navigation with smartphones is very popular. But in areas where no GNSS signal is found navigation could be useful. Examples are navigation in shopping malls, in big offices, in train stations or museums. The goal is to estimate the position in GNSS shaded areas to make navigation possible. The MEMS sensors (Micro Electro Mechanical System) installed...
Modelling new-born targets that spontaneously appear in the multi-target tracking scene is an indispensable yet challenging task for any multi-target tracker, which asks for a careful formulation of the target birth intensity (TBI) in random finite set based Bayesian filters. However, the TBI is widely assumed to hold for a constant magnitude that needs to be specified in advance, indicating a constant...
The objective of this paper is to approximate the unlabelled posterior random finite set (RFS) density in multitarget tracking (MTT) using particle filters (PFs). The unlabelled posterior can be equivalently represented by any labelled density that belongs to the posterior RFS family. For the limited number of particles used in practice, PFs that assume posterior independence among target states outperform...
In a conventional particle filter, the information update step can suffer from particle degeneracy if the likelihood function is concentrated on only a few particles. The homotopy particle flow method has been developed to implement the information update in an entirely different manner by using a particle flow function to migrate the particles to regions of the target state space that provide a good...
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