Particle-Filter Multi-Target Tracking Algorithm Based on Dynamic Salient Features

The conspicuity of different targets in image sequences taken by approaching sensors is addressed in applications such as the assessment of camouflage effectiveness or the performance evaluation of autonomous systems. In such evaluation processes the consideration of background characteristics is essential due to the propensity to confuse target and background signatures. Several discriminating features of target and background signature can be derived. Furthermore, the changing aspect and spatial resolution during an approach on a target have to be taken into account. Considering salient points in image sequences, we perform a nominal/actual value comparison by evaluating the receiver operating characteristic (ROC) curve for the detections in each image. Hence, reference regions for targets and backgrounds are provided for the entire image sequence by means of robust image registration. The consideration of the uncertainty for the temporal progression of the ROC curve enables hypothesis testing for well-founded statements about the significance of the determined distinctiveness of targets with respect to their background. The approach is neither restricted to images taken by IR sensors nor applicable to low level image analysis steps only, but can be considered as a general method for the assessment of feature evaluation and target distinctiveness. The analysis method proposed facilitates an objective comparison of object appearance with both, its relevant background and other targets, using different image analysis features. The feasibility and the usefulness of the approach are demonstrated with real data recorded with a FLIR sensor during a field trial on a bare and mock-up target.

This paper addresses the problem of designing an efficient and effective image sequence processing scheme that will successfully detect very small (point) targets in a cluttered background when both the target and clutter are moving through the image scene. The specific application area was detection of targets such as airplanes in infrared (IR) image sequences of a cloudy sky which have been taken by a stationary camera. In general we assume that targets are typically one to two pixels in extent and move only a fraction of a pixel per frame, are often low amplitude, and are found in scenes which also contain evolving clutter, e.g. clouds. Our algorithm is based on signal processing and detection theory, includes a perfect measurement performance analysis, and can be made computationally efficient compared to other approaches. Thus the algorithm could be applicable to other image sequence processing scenarios, using other acquisition systems besides IR, such as detection of small moving objects or structures in a biomedical or biological imaging scenario or the detection of satellites, meteors or other celestial bodies in night sky imagery acquired using a telescope. We present a GLRT solution, perfect measurement analysis including ROC curves, and results using real-world infrared data.

In the test range, it is a practical significance to use photoelectric theodolite to obtain the 3D pose of flying object by tracking and recording flying target in image sequences. In this paper, the image is obtained by using the two long-distance theodolite; namely, the target pose measurement is attained by dealing with the large baseline target images. Because the large baseline target is small, weak and details are not prominent. Image is enhanced by using the method of frequency-domain image enhancement. The camera self-calibration is based on the method of fundamental matrix; the 3D pose is calculated by feature point DLT algorithm and beam adjustment method. Experimental results showed that the measuring error of the attitude angle was less 0.1°, when the axis of the plane imaging was more than 500 pixels. According to the test results of the precision comparison, it was concluded that the mathematical model is correct and the algorithm is reasonable in improving effectively the measurement precision of the target pose.

This paper presents an approach for the detection of moving point targets in very low SNR based on purely time-domain analysis of pixels. The problem of detecting point targets in image sequences is formulated as a one-dimensional weak transient signal detection problem. The mathematical model of the transient signal is proposed. The key impact factors, such as the frame-rate, target velocity and SNR of the weak transient signal are studied. The detection ability is evaluated from the view of time-domain energy accumulation and correlation. The experimental results with both simulated and realworld data demonstrate that our approach can efficiently detect extremely low-SNR targets and the detection ability is decided by the key impact factors.

We describe an active contour based local energy minimization point distribution, behavior of orientation, relaxation iterative algorithm that estimates feature points of static target in image sequence. In our approach, we build a contour model of a target to get some of low-energy points kernels. The use of snake based line model results in more reliable convergence of the point local energy minimization. The algorithm uses auto-relation relation to give the behavior of orientation in a local kernel's window. It uses local window-based probability method to refine the current corresponding relations of scene kernels. Results are illustrated on real outdoor image sequence.

A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

A new man-made target tracking algorithm integrating features from (Forward Looking InfraRed) image sequence is presented based on particle filter. Firstly, a multiscale
\nfractal feature is used to enhance targets in FLIR images.
\nSecondly, the gray space feature is defined by Bhattacharyya
\ndistance between intensity histograms of the reference target and a sample target from MFF (Multi-scale Fractal Feature) image. Thirdly, the motion feature is obtained by differencing between two MFF images. Fourthly, a fusion coefficient can be automatically obtained by online feature selection method for features integrating based on fuzzy logic. Finally, a particle filtering framework is developed to fulfill the target tracking. Experimental results have shown that the proposed algorithm can accurately track weak or small man-made target in FLIR images with complicated background. The algorithm is effective, robust and satisfied to real time tracking.

Recently, there has been a surge of interest in incorporating neural networks into particle filters, e.g. differentiable particle filters, to perform joint sequential state estimation and model learning for nonlinear non-Gaussian state-space models in complex environments. Existing differentiable particle filters are mostly constructed with vanilla neural networks that do not allow density estimation. As a result, they are either restricted to a bootstrap particle filtering framework or employ predefined distribution families (e.g. Gaussian distributions), limiting their performance in more complex real-world scenarios. In this paper we present a differentiable particle filtering framework that uses (conditional) normalizing flows to build its dynamic model, proposal distribution, and measurement model. This not only enables valid probability densities but also allows the proposed method to adaptively learn these modules in a flexible way, without being restricted to predefined distribution families. We derive the theoretical properties of the proposed filters and evaluate the proposed normalizing flow-based differentiable particle filters’ performance through a series of numerical experiments.

State and parameter estimation of nonlinear stochastic systems by evolution strategies based particle filter

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

Aiming at the multiple targets recognition and tracking in SAR images, a robust feature extraction method and a combined recognition and tracking method for multi-class slow-moving targets based on visual cognition is presented in this paper. To obtain robust feature and high classification precision, a local multi-resolution analysis and feature extraction method based on the visual attention mechanism and a multiple kernel classifier is studied, which realizes the quick classification with high accuracy for multi-class image targets. According to the recognition result and the corresponding relationship of targets in the adjacent frames, the targets’ motion parameters are estimated utilizing the unscented Kalman filter (UKF) based on the “what” and “where” pathways information processing mechanism. As a result, the high performance tracking of multi-class slow-moving targets in complicated background is realized. The simulation results show that the feature extraction and recognition method has good robustness and high classification correct ratio, the combining recognition and tracking method also has high location precision.

An explicit variance reduction expression for the Rao-Blackwellised particle filter

A Penalty Function Approach to Bias Reduction in Non-linear Panel Models with Fixed Effects