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In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models within a continuous time setting using stochastic differential equations (SDE) that aim to mimic behavioural properties of groups. We also describe a possible way of modeling interactions between closely(More)
Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be(More)
In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models, within a continuous time setting, that aim to mimic behavioural properties of groups. We also describe two possible ways of modeling interactions between closely spaced targets using Markov Random Field(More)
In this paper, we present a simulation-based method for multitarget tracking and detection using sequential Monte Carlo (SMC), or particle filtering (PF) methods. The proposed approach is applicable to nonlinear and non-Gaussian models for the target dynamics and measurement likelihood, where the environment is characterised by high clutter rate and low(More)
In this paper, we present an online approach for joint initiation/termination and tracking for multiple targets with multiple sensors using sequential Monte Carlo (SMC) methods. There are several main contributions in the paper. The first contribution is the extension of the deterministic initiation and termination method proposed by the authors’ previous(More)
In this paper we propose a new approach for tracking manoeuvring objects using variable rate particle filters with multiple sensors. Unlike other approaches the proposed method assumes that the states change at different and unknown rates compared with the observation process, and hence is able to model parsimoniously the manoeuvring behaviours of an(More)
In this paper, we propose an extension of the soft-gating approach for measurement-to-target assignment for multitarget tracking. Given the latest observation and a set of multitarget particles, the proposed method combines efficient m-best 2D data assignment and sampling methods to compute a feasible measurement-to-target assignment with an associated(More)
In this paper we describe an efficient real-time tracking algorithm for multiple manoeuvring targets using particle filters. We combine independent partition filters with a Markov Random Field motion model to enable efficient and accurate tracking for interacting targets. A Poisson model is also used to model both targets and clutter measurements, avoiding(More)
In this paper, we present a new approach for online joint detection and tracking for multiple targets, using sequential Monte Carlo methods. We first use an observation clustering algorithm to find some regions of interest (ROIs), and then propose to initiate a new target or remove an existing track, based on the persistence information of these ROIs over(More)
In tracking applications, the target state (e.g., position, velocity) can be estimated by processing the measurements collected from all deployed sensors at a central node. The estimation performance significantly relies on the accuracy of the sensor positions/rotations when data fusion is conducted. Since in practice precise knowledge of this sensor(More)