Simultaneous 3D object tracking and camera parameter estimation by Bayesian methods and transdimensional MCMC sampling

Multi-camera 3D tracking systems with overlapping cameras represent a powerful mean for scene analysis, as they potentially allow greater robustness than monocular systems and provide useful 3D information about object location and movement. However, their performance relies on accurately calibrated camera networks, which is not a realistic assumption in real surveillance environments. Here, we introduce a multi-camera system for tracking the 3D position of a varying number of objects and simultaneously refin-ing the calibration of the network of overlapping cameras. Therefore, we introduce a Bayesian framework that combines Particle Filtering for tracking with recursive Bayesian estimation methods by means of adapted transdimensional MCMC sampling. Addi-tionally, the system has been designed to work on simple motion detection masks, making it suitable for camera networks with low transmission capabilities. Tests show that our approach allows a successful performance even when starting from clearly inaccurate camera calibrations, which would ruin conventional approaches.

Distributed target tracking based on belief propagation consensus

Distributed target tracking in wireless sensor networks (WSN) is an important problem, in which agreement on the target state can be achieved using conventional consensus methods, which take long to converge. We propose distributed particle filtering based on belief propagation (DPF-BP) consensus, a fast method for target tracking. According to our simulations, DPF-BP provides better performance than DPF based on standard belief consensus (DPF-SBC) in terms of disagreement in the network. However, in terms of root-mean square error, it can outperform DPF-SBC only for a specific number of consensus iterations.

On-road visual vehicle tracking using Markov chain Monte Carlo with metropolis sampling

In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain given by inverse perspective mapping (IPM) to define a fast and efficient likelihood model. Additionally, the method encompasses an interaction model using Markov Random Fields (MRF) that allows treatment of dependencies between the motions of targets. The proposed method is tested in highway sequences and compared to state-of-the-art methods for vehicle tracking, i.e., independent target tracking with Kalman filtering (KF) and joint tracking with particle filtering. The results showed fewer tracking failures using the proposed method.

Multirobot Tethering for Localization and Control

Particle filtering has proven to be an effective localization method for wheeled autonomous vehicles. For a given map, a sensor model, and observations, occasions arise where the vehicle could equally likely be in many locations of the map. Because particle filtering algorithms may generate low confidence pose estimates under these conditions, more robust localization strategies are required to produce reliable pose estimates. This becomes more critical if the state estimate is an integral part of system control. We investigate the use of particle filter estimation techniques on a hovercraft vehicle. The marginally stable dynamics of a hovercraft require reliable state estimates for proper stability and control. We use the Monte Carlo localization method, which implements a particle filter in a recursive state estimate algorithm. An H-infinity controller, designed to accommodate the latency inherent in our state estimation, provides stability and controllability to the hovercraft. In order to eliminate the low confidence estimates produced in certain environments, a multirobot system is designed to introduce mobile environment features. By tracking and controlling the secondary robot, we can position the mobile feature throughout the environment to ensure a high confidence estimate, thus maintaining stability in the system. A laser rangefinder is the sensor the hovercraft uses to track the secondary robot, observe the environment, and facilitate successful localization and stability in motion.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

A computational fluid dynamic study on the filtering mechanics in suspension feeding marine invertebrates

Les suspensivores ont la tâche importante de séparer les particules de l'eau. Bien qu'une grande gamme de morphologies existe pour les structures d'alimentation, elles sont pratiquement toutes constituées de rangées de cylindres qui interagissent avec leur environnement fluide. Le mécanisme de capture des particules utilisé dépend des contraintes morphologiques, des besoins énergétiques et des conditions d'écoulement. Comme nos objectifs étaient de comprendre ces relations, nous avons eu recours à des études de comparaison pour interpréter les tendances en nature et pour comprendre les conditions qui provoquent de nouveaux fonctionnements. Nous avons utilisé la dynamique des fluides numérique (computational fluid dynamics, CFD) pour créer des expériences contrôlées et pour simplifier les analyses. Notre première étude démontre que les coûts énergétiques associés au pompage dans les espaces petits sont élevés. De plus, le CFD suggère que les fentes branchiales des ptérobranches sont des structures rudimentaires, d'un ancêtre plus grande. Ce dernier point confirme l'hypothèse qu'un ver se nourrit par filtration tel que l'ancêtre des deuterostomes. Notre deuxième étude détermine la gamme du nombre de Reynolds number critique où la performance d'un filtre de balane change. Quand le Re est très bas, les différences morphologiques n'ont pas un grand effet sur le fonctionnement. Cependant, une pagaie devient une passoire lorsque le Re se trouve entre 1 et 3,5. Le CFD s’est dévoilé être un outil très utile qui a permis d’obtenir des détails sur les microfluides. Ces études montrent comment la morphologie et les dynamiques des fluides interagissent avec la mécanisme de capture ou de structures utilisées, ainsi que comment des petits changements de taille, de forme, ou de vitesse d'écoulement peuvent conduire à un nouveau fonctionnement.

A mechatronic system applied to wear particle analysis

This paper describes the development of a mechatronic system for a predictive maintenance grounded on wear particle analysis. The reckoning of wear particles containing in lubricating industrial oils brings the image acquisition system into being. The ISO 4406:1999 standard is a guide to establish the counting and evaluation processes of particles. The system applied to the acquisition and analysis of the data consists of a digital camera, a monocular microscope and an oil filtering system. A computational program was developed with the application of Visual Microsoft C++ in a way to detain the oil sample image from the microscope slide to the computer screen. Quantitative analyses of the wear debris particles bulk are exploited applying a graphical interface that was developed to render the image processing of the sample test. The implemented system has a reachable cost thus it can be applied for schooling goals and for bolstering laboratories of minor industries and medium size companies.

Reference Values For High-density Lipoprotein Particle Size And Volume By Dynamic Light Scattering In A Brazilian Population Sample And Their Relationships With Metabolic Parameters.

Current data indicate that the size of high-density lipoprotein (HDL) may be considered an important marker for cardiovascular disease risk. We established reference values of mean HDL size and volume in an asymptomatic representative Brazilian population sample (n=590) and their associations with metabolic parameters by gender. Size and volume were determined in HDL isolated from plasma by polyethyleneglycol precipitation of apoB-containing lipoproteins and measured using the dynamic light scattering (DLS) technique. Although the gender and age distributions agreed with other studies, the mean HDL size reference value was slightly lower than in some other populations. Both HDL size and volume were influenced by gender and varied according to age. HDL size was associated with age and HDL-C (total population); non- white ethnicity and CETP inversely (females); HDL-C and PLTP mass (males). On the other hand, HDL volume was determined only by HDL-C (total population and in both genders) and by PLTP mass (males). The reference values for mean HDL size and volume using the DLS technique were established in an asymptomatic and representative Brazilian population sample, as well as their related metabolic factors. HDL-C was a major determinant of HDL size and volume, which were differently modulated in females and in males.

Metamorfose planar via métodos Level Set e Particle Level Set para a reconstrução de superfícies tridimensionais

Evolving interfaces were initially focused on solutions to scientific problems in Fluid Dynamics. With the advent of the more robust modeling provided by Level Set method, their original boundaries of applicability were extended. Specifically to the Geometric Modeling area, works published until then, relating Level Set to tridimensional surface reconstruction, centered themselves on reconstruction from a data cloud dispersed in space; the approach based on parallel planar slices transversal to the object to be reconstructed is still incipient. Based on this fact, the present work proposes to analyse the feasibility of Level Set to tridimensional reconstruction, offering a methodology that simultaneously integrates the proved efficient ideas already published about such approximation and the proposals to process the inherent limitations of the method not satisfactorily treated yet, in particular the excessive smoothing of fine characteristics of contours evolving under Level Set. In relation to this, the application of the variant Particle Level Set is suggested as a solution, for its intrinsic proved capability to preserve mass of dynamic fronts. At the end, synthetic and real data sets are used to evaluate the presented tridimensional surface reconstruction methodology qualitatively.

Metamorfose planar via métodos Level Set e Particle Level Set para a reconstrução de superfícies tridimensionais

Evolving interfaces were initially focused on solutions to scientific problems in Fluid Dynamics. With the advent of the more robust modeling provided by Level Set method, their original boundaries of applicability were extended. Specifically to the Geometric Modeling area, works published until then, relating Level Set to tridimensional surface reconstruction, centered themselves on reconstruction from a data cloud dispersed in space; the approach based on parallel planar slices transversal to the object to be reconstructed is still incipient. Based on this fact, the present work proposes to analyse the feasibility of Level Set to tridimensional reconstruction, offering a methodology that simultaneously integrates the proved efficient ideas already published about such approximation and the proposals to process the inherent limitations of the method not satisfactorily treated yet, in particular the excessive smoothing of fine characteristics of contours evolving under Level Set. In relation to this, the application of the variant Particle Level Set is suggested as a solution, for its intrinsic proved capability to preserve mass of dynamic fronts. At the end, synthetic and real data sets are used to evaluate the presented tridimensional surface reconstruction methodology qualitatively.

FILTERING TECHNIQUES APPLIED ON RAW CARRIER PHASE FOR GPS DETECTING SMALL DYNAMIC DISPLACEMENTS

This work is part of a research under construction since 2000, in which the main objective is to measure small dynamic displacements by using L1 GPS receivers. A very sensible way to detect millimetric periodic displacements is based on the Phase Residual Method (PRM). This method is based on the frequency domain analysis of the phase residuals resulted from the L1 double difference static data processing of two satellites in almost orthogonal elevation angle. In this article, it is proposed to obtain the phase residuals directly from the raw phase observable collected in a short baseline during a limited time span, in lieu of obtaining the residual data file from regular GPS processing programs which not always allow the choice of the aimed satellites. In order to improve the ability to detect millimetric oscillations, two filtering techniques are introduced. One is auto-correlation which reduces the phase noise with random time behavior. The other is the running mean to separate low frequency from the high frequency phase sources. Two trials have been carried out to verify the proposed method and filtering techniques. One simulates a 2.5 millimeter vertical antenna displacement and the second uses the GPS data collected during a bridge load test. The results have shown a good consistency to detect millimetric oscillations.

Particle competition for complex network community detection

In many real situations, randomness is considered to be uncertainty or even confusion which impedes human beings from making a correct decision. Here we study the combined role of randomness and determinism in particle dynamics for complex network community detection. In the proposed model, particles walk in the network and compete with each other in such a way that each of them tries to possess as many nodes as possible. Moreover, we introduce a rule to adjust the level of randomness of particle walking in the network, and we have found that a portion of randomness can largely improve the community detection rate. Computer simulations show that the model has good community detection performance and at the same time presents low computational complexity. (C) 2008 American Institute of Physics.

Reduction of chaotic particle transport driven by drift waves in sheared flows

Investigations of chaotic particle transport by drift waves propagating in the edge plasma of tokamaks with poloidal zonal flow are described. For large aspect ratio tokamaks, the influence of radial electric field profiles on convective cells and transport barriers, created by the nonlinear interaction between the poloidal flow and resonant waves, is investigated. For equilibria with edge shear flow, particle transport is seen to be reduced when the electric field shear is reversed. The transport reduction is attributed to the robust invariant tori that occur in nontwist Hamiltonian systems. This mechanism is proposed as an explanation for the transport reduction in Tokamak Chauffage Alfven Bresilien [R. M. O. Galvao , Plasma Phys. Controlled Fusion 43, 1181 (2001)] for discharges with a biased electrode at the plasma edge.

Particle-species dependent modification of jet-induced correlations in Au plus Au collisions at root s(NN)=200 GeV

Measurements in Au + Au collisions at root s(NN) = 200 GeV of jet correlations for a trigger hadron at intermediate transverse momentum (p(T,trig)) with associated mesons or baryons at lower p(T,assoc) indicate strong modification of the away-side jet. The ratio of jet-associated baryons to mesons increases with centrality and p(T,assoc). For the most central collisions, the ratio is similar to that for inclusive measurements. This trend is incompatible with in-vacuum fragmentation but could be due to jetlike contributions from correlated soft partons, which recombine upon hadronization.

Topology Studies of Hydrodynamics Using Two-Particle Correlation Analysis

The effects of fluctuating initial conditions are studied in the context of relativistic heavy ion collisions where a rapidly evolving system is formed. Two-particle correlation analysis is applied to events generated with the NEXSPHERIO hydrodynamic code, starting with fluctuating nonsmooth initial conditions (IC). The results show that the nonsmoothness in the IC survives the hydroevolution and can be seen as topological features of the angular correlation function of the particles emerging from the evolving system. A long range correlation is observed in the longitudinal direction and in the azimuthal direction a double peak structure is observed in the opposite direction to the trigger particle. This analysis provides clear evidence that these are signatures of the combined effect of tubular structures present in the IC and the proceeding collective dynamics of the hot and dense medium.