8 accepted papers
This paper proposes a reversible jump Markov chain Monte Carlo method that provides efficient inference for the general problem of pulse fitting. In particular, it minimises the potential of an adopted parametric model overfitting to the (noisy) data via the inclusion of a peak proximity parameter.…
Multi-target tracking of agile targets can be limited by choice of dynamical models. This is typically overcome by using sophisticated non-Gaussian and/or nonlinear motion models, and complex data association schemes. Here we aim to tackle scenarios when tracking (semi-)autonomous systems, such as d…
In group object tracking, the identification of the group leader can be highly beneficial for predicting the intention and future manoeuvres of objects as well as learning the underlying group behaviour traits. This paper presents an online approach for inferring dominant entities in tracked groups…
In this paper, we introduce a generic Bayesian framework for inferring the intent of a tracked object, as early as possible, based on the available partial sensory observations. It treats the prediction problem, i.e. not estimating the object state such as position, within an object tracking formula…
In this paper we introduce a novel class of efficient multichannel adaptive filtering algorithms for sparse FIR systems. By suitably integrating ideas from compressed sensing and adaptive filter theory, this class of algorithms allows to significantly reduce the actual number of adaptive coefficient…
This paper proposes a probabilistic intent inference approach that is significantly more computationally efficient than other existing bridging-distributions-based predictors. It sequentially determines the probabilities of all possible destinations of a tracked object, whose motion is modelled by a…
This paper introduces a novel method of estimating the Fourier transform of deterministic continuous-time signals from a finite number N of their nonuniformly spaced measurements. These samples, located at a mixture of deterministic and random time instants, are collected at sub-Nyquist rates since…
We propose a novel probabilistic inference approach that permits predicting, well in advance, the intended destination of a pointing gesture aimed at selecting an icon on an in-vehicle interactive display. It models the partial 3D pointing track as a Markov bridge terminating at a nominal destinatio…