Deep Convolutional Correlation Iterative Particle Filter for Visual Tracking

This work proposes a novel framework for visual tracking based on the integration of an iterative particle filter, a deep convolutional neural network, and a correlation filter. The iterative particle filter enables the particles to correct themselves and converge to the correct target position. We employ a novel strategy to assess the likelihood of the particles after the iterations by applying K-means clustering. Our approach ensures a consistent support for the posterior distribution. Thus, we do not need to perform resampling at every video frame, improving the utilization of prior distribution information. Experimental results on two different benchmark datasets show that our tracker performs favorably against state-of-the-art methods.Comment: 28 pages, 9 figures, 1 tabl

An iterative Bayesian filtering framework for fast and automated calibration of DEM models

The nonlinear, history-dependent macroscopic behavior of a granular material is rooted in the micromechanics between constituent particles and irreversible, plastic deformations reflected by changes in the microstructure. The discrete element method (DEM) can predict the evolution of the microstructure resulting from interparticle interactions. However, micromechanical parameters at contact and particle levels are generally unknown because of the diversity of granular materials with respect to their surfaces, shapes, disorder and anisotropy. The proposed iterative Bayesian filter consists in recursively updating the posterior distribution of model parameters and iterating the process with new samples drawn from a proposal density in highly probable parameter spaces. Over iterations the proposal density is progressively localized near the posterior modes, which allows automated zooming towards optimal solutions. The Dirichlet process Gaussian mixture is trained with sparse and high dimensional data from the previous iteration to update the proposal density. As an example, the probability distribution of the micromechanical parameters is estimated, conditioning on the experimentally measured stress–strain behavior of a granular assembly. Four micromechanical parameters, i.e., contact-level Young’s modulus, interparticle friction, rolling stiffness and rolling friction, are chosen as strongly relevant for the macroscopic behavior. The a priori particle configuration is obtained from 3D X-ray computed tomography images. The a posteriori expectation of each micromechanical parameter converges within four iterations, leading to an excellent agreement between the experimental data and the numerical predictions. As new result, the proposed framework provides a deeper understanding of the correlations among micromechanical parameters and between the micro- and macro-parameters/quantities of interest, including their uncertainties. Therefore, the iterative Bayesian filtering framework has a great potential for quantifying parameter uncertainties and their propagation across various scales in granular materials

Deep Convolutional Correlation Particle Filter for Visual Tracking

In this dissertation, we explore the advantages and limitations of the application of sequential Monte Carlo methods to visual tracking, which is a challenging computer vision problem. We propose six visual tracking models, each of which integrates a particle filter, a deep convolutional neural network, and a correlation filter. In our first model, we generate an image patch corresponding to each particle and use a convolutional neural network (CNN) to extract features from the corresponding image region. A correlation filter then computes the correlation response maps corresponding to these features, which are used to determine the particle weights and estimate the state of the target. We then introduce a particle filter that extends the target state by incorporating its size information. This model also utilizes a new adaptive correlation filtering approach that generates multiple target models to account for potential model update errors. We build upon that strategy to devise an adaptive particle filter that can decrease the number of particles in simple frames in which there is no challenging scenarios and the target model closely reflects the current appearance of the target. This strategy allows us to reduce the computational cost of the particle filter without negatively impacting its performance. This tracker also improves the likelihood model by generating multiple target models using varying model update rates based on the high-likelihood particles. We also propose a novel likelihood particle filter for CNN-correlation visual trackers. Our method uses correlation response maps to estimate likelihood distributions and employs these likelihoods as proposal densities to sample particles. Additionally, our particle filter searches for multiple modes in the likelihood distribution using a Gaussian mixture model. We further introduce an iterative particle filter that performs iterations to decrease the distance between particles and the peaks of their correlation maps which results in having a few more accurate particles in the end of iterations. Applying K-mean clustering method on the remaining particles determine the number of the clusters which is used in evaluation step and find the target state. Our approach ensures a consistent support for the posterior distribution. Thus, we do not need to perform resampling at every video frame, improving the utilization of prior distribution information. Finally, we introduce a novel framework which calculates the confidence score of the tracking algorithm at each video frame based on the correlation response maps of the particles. Our framework applies different model update rules according to the calculated confidence score, reducing tracking failures caused by model drift. The benefits of each of the proposed techniques are demonstrated through experiments using publicly available benchmark datasets

Automatic vehicle detection and tracking in aerial video

This thesis is concerned with the challenging tasks of automatic and real-time vehicle detection and tracking from aerial video. The aim of this thesis is to build an automatic system that can accurately localise any vehicles that appear in aerial video frames and track the target vehicles with trackers. Vehicle detection and tracking have many applications and this has been an active area of research during recent years; however, it is still a challenge to deal with certain realistic environments. This thesis develops vehicle detection and tracking algorithms which enhance the robustness of detection and tracking beyond the existing approaches. The basis of the vehicle detection system proposed in this thesis has different object categorisation approaches, with colour and texture features in both point and area template forms. The thesis also proposes a novel Self-Learning Tracking and Detection approach, which is an extension to the existing Tracking Learning Detection (TLD) algorithm. There are a number of challenges in vehicle detection and tracking. The most difficult challenge of detection is distinguishing and clustering the target vehicle from the background objects and noises. Under certain conditions, the images captured from Unmanned Aerial Vehicles (UAVs) are also blurred; for example, turbulence may make the vehicle shake during flight. This thesis tackles these challenges by applying integrated multiple feature descriptors for real-time processing. In this thesis, three vehicle detection approaches are proposed: the HSV-GLCM feature approach, the ISM-SIFT feature approach and the FAST-HoG approach. The general vehicle detection approaches used have highly flexible implicit shape representations. They are based on training samples in both positive and negative sets and use updated classifiers to distinguish the targets. It has been found that the detection results attained by using HSV-GLCM texture features can be affected by blurring problems; the proposed detection algorithms can further segment the edges of the vehicles from the background. Using the point descriptor feature can solve the blurring problem, however, the large amount of information contained in point descriptors can lead to processing times that are too long for real-time applications. So the FAST-HoG approach combining the point feature and the shape feature is proposed. This new approach is able to speed up the process that attains the real-time performance. Finally, a detection approach using HoG with the FAST feature is also proposed. The HoG approach is widely used in object recognition, as it has a strong ability to represent the shape vector of the object. However, the original HoG feature is sensitive to the orientation of the target; this method improves the algorithm by inserting the direction vectors of the targets. For the tracking process, a novel tracking approach was proposed, an extension of the TLD algorithm, in order to track multiple targets. The extended approach upgrades the original system, which can only track a single target, which must be selected before the detection and tracking process. The greatest challenge to vehicle tracking is long-term tracking. The target object can change its appearance during the process and illumination and scale changes can also occur. The original TLD feature assumed that tracking can make errors during the tracking process, and the accumulation of these errors could cause tracking failure, so the original TLD proposed using a learning approach in between the tracking and the detection by adding a pair of inspectors (positive and negative) to constantly estimate errors. This thesis extends the TLD approach with a new detection method in order to achieve multiple-target tracking. A Forward and Backward Tracking approach has been proposed to eliminate tracking errors and other problems such as occlusion. The main purpose of the proposed tracking system is to learn the features of the targets during tracking and re-train the detection classifier for further processes. This thesis puts particular emphasis on vehicle detection and tracking in different extreme scenarios such as crowed highway vehicle detection, blurred images and changes in the appearance of the targets. Compared with currently existing detection and tracking approaches, the proposed approaches demonstrate a robust increase in accuracy in each scenario

Desarrollo de estimadores de estado empleando la librería de simulación física Simbody

RESUMEN: La evolución de la computadora ha hecho posible la simulación de sistemas multicuerpo en tiempo real y el uso de estimadores de estado para inferir parámetros ocultos del estado del sistema. Entre estos estimadores de estado, se encuentran los filtros de Kalman, una opción eficiente y ampliamente usada, pero que no fueron formuladas para sistemas de segundo orden, no lineales y con restricciones, como suelen ser los sistemas multicuerpo. En dichos casos, se suelen utilizar distribuciones no paramétricas, como es el filtro de partículas. En este trabajo se pretende desarrollar una librería en C++ para la implementación de estimadores de estado, utilizando la técnica de filtro de partículas y la librería de simulación física Simbody. ABSTRACT: The evolution of the computer has made possible to achieve real-time simulation of multibody systems and using state observers to infer hidden state variables of them. Kalman filters are an efficient and vastly used option among these states observers, originally not formulated for second-order, non-linear and restricted systems though, such as multibody systems. In those cases, non-parametric distributions are often used, particle filters as an example. The aim of this work is developing a C++ library to implement a state observer, using the particle filter technique and Simbody, a multibody physics library