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Contour tracking for a single source emission is addressed in this paper. This problem is solved by estimating the contour boundary positions using a set of particle filters. The use of Sequential Monte Carlo techniques enables the tracking to performed when the measurements are noisy and the tracking results also includes the estimation uncertainly. The proposed technique is illustrated for a SCIPUFF...
The particle filtering technique with multiple cues such as colour, texture and edges as observation features is a powerful technique for tracking deformable objects in image sequences with complex backgrounds. In this paper, our recent work (Brasnett et al., 2005) on single object tracking using particle filters is extended to multiple objects. In the proposed scheme, track initialisation is embedded...
This paper presents a particle filtering algorithm for multiple object tracking. The proposed particle filter (PF) embeds a data association technique based on the joint probabilistic data association (JPDA) which handles the uncertainty of the measurement origin.
In this paper we introduce a novel sequential Monte Carlo technique, which is based on the family of symmetric alphastable (SalphaS) distributions. Sequential Bayesian estimation generally involves recursive estimation of filtering and predictive distributions of unobserved signals from their noisy measurements. In our proposed algorithm, the relevant density functions are approximated by particles...
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