This article presents an approach to model and to design a temperature controller for the PROMES's 1MW solar furnace. The aim is to evaluate the operation the 1MW solar furnace in the framework of automatic temperature stress experiments, where material samples are submitted to temperature cycles. In particular, it must track the temperature profiles selected by the user and, must compensate temperature changes induced by sun power variability caused by weather conditions. A simplified model is developed to capture the main dynamics of the solar furnace. This includes the temperature dynamic model of the material sample and a model for the on-off operation of the heliostats, that have transport delays and impose quantized power levels. The controller is designed in the discrete time framework. It uses the temperature profile and the information that is provided by temperature and direct normal irradiance sensors to schedule on real-time the number of heliostats.

This paper considers the representation of human trajectories in video signals. These trajectories are modeled by switched dynamical models, based on motion fields that drive the pedestrian during consecutive time intervals. This paper addresses the estimation of uncertainty in trajectory generation by using space-varying covariance matrices estimated from the video data. Experimental results show that the proposed model outperforms previous methods, based on static and isotropic covariance matrices.

This paper proposes an iterative natural gradient algorithm to perform the optimization of switching probabilities in a space-varying hidden Markov model, in the context of human activity recognition in long-range surveillance. The proposed method is a version of the gradient method, developed under an information geometric viewpoint, where the usual Euclidean metric is replaced by a Riemannian metric on the space of transition probabilities. It is shown that the change in metric provides advantages over more traditional approaches, namely: 1) it turns the original constrained optimization into an unconstrained optimization problem; 2) the optimization behaves asymptotically as a Newton method and yields faster convergence than other methods for the same computational complexity; and 3) the natural gradient vector is an actual contravariant vector on the space of probability distributions for which an interpretation as the steepest descent direction is formally correct. Experiments on synthetic and real-world problems, focused on human activity recognition in long-range surveillance settings, show that the proposed methodology compares favorably with the state-of-the-art algorithms developed for the same purpose.

This paper presents a novel manifold learning algorithm for high-dimensional data sets. The scope of the application focuses on the problem of motion tracking in video sequences. The framework presented is twofold. First, it is assumed that the samples are time ordered, providing valuable information that is not presented in the current methodologies. Second, the manifold topology comprises multiple charts, which contrasts to the most current methods that assume one single chart, being overly restrictive. The proposed algorithm, Gaussian process multiple local models (GP-MLM), can deal with arbitrary manifold topology by decomposing the manifold into multiple local models that are probabilistic combined using Gaussian process regression. In addition, the paper presents a multiple filter architecture where standard filtering techniques are integrated within the GP-MLM. The proposed approach exhibits comparable performance of state-of-the-art trackers, namely multiple model data association and deep belief networks, and compares favorably with Gaussian process latent variable models. Extensive experiments are presented using real video data, including a publicly available database of lip sequences and left ventricle ultrasound images, in which the GP-MLM achieves state of the art results.

Many approaches to trajectory analysis, such as clustering or classification, use probabilistic generative models, thus not requiring trajectory alignment/registration. Switched linear dynamical models (e.g., HMMs) have been used in this context, due to their ability to describe different motion regimes. However, these models are not suitable for handling space-dependent dynamics that are more naturally captured by nonlinear models. As is well known, these are more difficult to identify. In this paper, we propose a new way of modeling trajectories, based on a mixture of parametric motion vector fields that depend on a small number of parameters. Switching among these fields follows a probabilistic mechanism, characterized by a field of stochastic matrices. This approach allows representing a wide variety of trajectories and modeling space-dependent behaviors without using global nonlinear dynamical models. Experimental evaluation is conducted in both synthetic and real scenarios. The latter concerning with human trajectory modeling for activity classification, a central task in video surveillance.

Video cameras monitoring human activities in public spaces are commonplace in cities worldwide. Such monitoring task is important for safety and security purposes but is also extremely challenging. In this paper, we propose a class of algorithms for far-field human activity recognition, a central task in video surveillance. More specifically, we explore a class of parametric motion vector fields learned from the trajectories described by people in real-world scenarios. The work proposed herein is a space dependent framework, in sense that the vector fields depend on the pedestrian position. Thus, the model is flexible leading to an expressive description of complex trajectories. Also, a model selection strategy is addressed to automatically choose the appropriate number of underlying motion fields presented in the trajectories. Experimental evaluation is conducted in real settings testifying the usefulness of the proposed approach for human activity recognition.

Many approaches to trajectory analysis tasks (such as clustering or classification) use probabilistic generative models, thus not requiring trajectory alignment/registration. Switched linear dynamical models (e.g., HMMs) have been used in this context, due to their ability to describe different motion regimes. However, this type of models is not suitable for handling space-dependent dynamics, that are more naturally captured by non-linear models. As is well known, these are more difficult to identify. We propose a new way of modeling trajectories, based on a mixture of parametric motion vector fields that depend on a small number of parameters. Switching among these fields follows a probabilistic mechanism, characterized by a field of stochastic matrices. This approach allows representing a wide variety of trajectories and modeling space-dependent behaviors without using global non-linear dynamical models. The proposed model is applied to human trajectory modeling, a central task in video surveillance.

A parallel computer architecture and a distributed software platform for automation and control of general anesthesia is proposed in this paper. The system is a prototype research platform, intended to help on the development, simulation and test of new control algorithms for general anesthesia. It must be safe when used in real tests and flexible enough to allow the integration of new software modules. The system is composed by two computers, with the specific tasks of anesthesia control and process supervision. The platform makes use of TANGO, a specialized framework for distributed control systems, which provides software mechanisms useful to fulfill the project requirements. The architecture and the set of mechanisms proposed in this paper provide a high degree of flexibility to research on control algorithms, while ensuring the safeness of the whole procedure.

The problem of non-linear dimension reduction is relevant to many different areas of knowledge. While the linear case can be solved by variations of PCA, the non-linear case is more complex. Recent advances incorporate geometrical information by estimating a manifold that approximates the data. The paper follows that trend and tackles some limitations of existing approaches -arbitrary topology and curvature of the manifold, unknown intrinsic dimension and, for mixture models, unknown number of mixture components. An algorithm, designated TBA (tangent bundle approximation), is presented that addresses the enumerated difficulties and is faster than existing methods, in datasets of a few thousand points. The motivation behind TBA is to perform motion tracking in video sequences, but the algorithm can be applied in a wide class of problems. The paper starts with a brief review of related work and then describes the TBA approach in detail. The algorithm is then subjected to comparative evaluation.

There has been a surge of interest in learning non-linear manifold models to approximate high-dimensional data. Both for computational complexity reasons and for generalization capability, sparsity is a desired feature in such models. This usually means dimensionality reduction, which naturally implies estimating the intrinsic dimension, but it can also mean selecting a subset of the data to use as landmarks, which is especially important because many existing algorithms have quadratic complexity in the number of observations. This paper presents an algorithm for selecting landmarks, based on LASSO regression, which is well known to favor sparse approximations because it uses regularization with an l1 norm. As an added benefit, a continuous manifold parameterization, based on the landmarks, is also found. Experimental results with synthetic and real data illustrate the algorithm.

This paper addresses long term tracking of multiple objects with occlusions. Bayesian networks are used to model the interaction among the detected tracks and for conflict management, allowing the tracker to update the labelling decisions as soon as new information is available. If several objects overlap in the image domain and then become separated in the next frames, the proposed algorithm is able to accumulate the evidence extracted from the images and to disambiguate the competing labels. The system also provides a confidence degree for each labelling decision. Experimental results are provided to illustrate the performance of the proposed method for long term tracking of multiple pedestrians.

A comparison of some fuzzy model-based adaptive-predictive control strategies is provided. The considered strategies are based on the minimization of a predefined long-range cost functional reflecting the tracking error between the reference to be followed and the plant's output. They begin at the current time instant and continue up to a given time horizon in the future. A fuzzy model of the plant is used for forecasting. A particular control strategy is specified by selecting the type of fuzzy model to be used as a plant description, the algorithm used for model parameter estimation and the assumptions required for the long-range cost minimization. The simulation results presented show that the control performance depends not only on the assumption required for multi-step optimization, but most notably it depends on the convergence rate of the parameter estimation algorithm used in the online model identification.

This paper addresses the problem of tracking of objects with complex shape or motion dynamics. The approach followed relies on multiple models based on Gaussian mixtures and hidden Markov models. A tracking algorithm derived from nonlinear filtering is presented and illustrated in two situations. In the first, two points moving independently along a line are tracked, only one being observed at each time. In the second, two-dimensional objects are tracked, under severe shape deformations. Unlike other multi-model approaches, the proposed method relies on parametric techniques providing an efficient tool to update shape and motion estimates.

Object tracking based on multipole models has been previously advocated as a way to tackle sudden changes of shape or motion parameters. The paper addresses the estimation of time-varying parameters described by a bank of stochastic models switched according to a probabilistic mechanism (Markov chain). A state estimation algorithm is proposed, based on the propagation of Gaussian mixtures in a multi-model framework.

The parameters to be estimated in dynamical image analysis are usually restricted to a subset of the Euclidean space. This constraint is often neglected in the design of image analysis algorithms although it allows one to achieve significant improvements. This paper describes an algorithm for the estimation of parameter evolution on manifolds exploiting three sources of information: the manifold geometry, the motion model and the sensor model. Examples are provided to show that the proposed algorithm outperforms classic estimation methods in these problems.