Multiple object tracking using evolutionary MCMC-based particle algorithms

Algorithms are presented for detection and tracking of multiple clusters of co-ordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithms maintain a discrete approximation of the filtering density of the clusters' state. The filters' tracking efficiency is enhanced by incorporating various sampling improvement strategies into the basic Metropolis-Hastings scheme. Thus, an evolutionary stage consisting of two primary steps is introduced: 1) producing a population of different chain realizations, and 2) exchanging genetic material between samples in this population. The performance of the resulting evolutionary filtering algorithms is demonstrated in two different settings. In the first, both group and target properties are estimated whereas in the second, which consists of a very large number of targets, only the clustering structure is maintained. © 2009 IFAC.

MCMC-based tracking and identification of leaders in groups

We present a novel framework for identifying and tracking dominant agents in groups. Our proposed approach relies on a causality detection scheme that is capable of ranking agents with respect to their contribution in shaping the system's collective behaviour based exclusively on the agents' observed trajectories. Further, the reasoning paradigm is made robust to multiple emissions and clutter by employing a class of recently introduced Markov chain Monte Carlo-based group tracking methods. Examples are provided that demonstrate the strong potential of the proposed scheme in identifying actual leaders in swarms of interacting agents and moving crowds. © 2011 IEEE.

The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. Based on a Markov chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm's performance is assessed and demonstrated in both synthetic and realistic tracking scenarios. © 2012 Elsevier Ltd. All rights reserved.

Estimation of soil properties and deformations in staged constructions based on mcmc method

This paper presents a Bayesian probabilistic framework to assess soil properties and model uncertainty to better predict excavation-induced deformations using field deformation data. The potential correlations between deformations at different depths are accounted for in the likelihood function needed in the Bayesian approach. The proposed approach also accounts for inclinometer measurement errors. The posterior statistics of the unknown soil properties and the model parameters are computed using the Delayed Rejection (DR) method and the Adaptive Metropolis (AM) method. As an application, the proposed framework is used to assess the unknown soil properties of multiple soil layers using deformation data at different locations and for incremental excavation stages. The developed approach can be used for the design of optimal revisions for supported excavation systems. © 2010 ASCE.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

The Implications of Bach's Introduction of New Fugal Techniques and Procedures in the Well-Tempered Clavier Book Two

Among the fugues of the WTC II, there are some fugal techniques and procedures that were not explored in the first book. Here, the ‘fugal techniques’ include parallel entries (as used in the fugues in D-sharp minor, G minor and B-flat minor) and double counterpoint at the tenth or twelfth as well as fifteenth (as used in the fugues in G minor and B major). The ‘fugal procedures’, on the other hand, refer to meticulously planned multi-exposition architecture (as seen in the fugues in F-sharp minor exploiting two subsidiary subjects, and B-flat minor exploiting inversion and stretto) and a form in which the appearance of the subsidiary subject is gradually predicted in the fugal discourse (viz. C-sharp minor, G-sharp minor and B major). All these new ideas helped Bach to write more dramatic, more profound fugues for WTC II. The paper will consider how Bach came to acquire the new techniques and to use them in such ways, and what motivated him to adopt these new compositional approaches. Do they offer any clues for our better understanding of why Bach compiled the WTC II?

Veiled Aspects of Bach Reception in the Long Nineteenth Century Exposed through a Macro-examination of Printed Music: with Particular Focus on The Well-Tempered Clavier

It presents questions that have arisen as a result of more than 20 years of collecting the data for the Bach Bibliography. Taking editions of The Well-Tempered Clavier as an example and using figures and graphs extracted from the Bach Bibliography, Tomita explores the various facets of the work's reception including its market appeal, the ambitions that steered its editors and publishers, and trends in its interpretation.