Download or read book Efficient Multi Target Tracking Using Graphical Models written by Zhexu Michael Chen and published by . This book was released on 2008 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this thesis is to develop a new framework for Multi-Target Tracking (MTT) algorithms that are distinguished by the use of statistical machine learning techniques. MTT is a crucial problem for many important practical applications such as military surveillance. Despite being a well-studied research problem, MTT remains challenging, mostly because of the challenges of computational complexity faced by current algorithms. Taking a very di®erent approach from any existing MTT algorithms, we use the formalism of graphical models to model the MTT problem according to its probabilistic structure, and subsequently develop e±cient, approximate message passing algorithms to solve the MTT problem. Our modeling approach is able to take into account issues such as false alarms and missed detections. Although exact inference is intractable in graphs with a mix of both discrete and continuous random variables, such as the ones for MTT, our message passing algorithms utilize e±cient particle reduction techniques to make approximate inference tractable on these graphs. Experimental results show that our approach, while maintaining acceptable tracking quality, leads to linear running time complexity with respect to the duration of the tracking window. Moreover, our results demonstrate that, with the graphical model structure, our approach can easily handle special situations, such as out-of-sequence observations and track stitching.
Download or read book Multi target Tracking with Probabilistic Graphical Models written by Martin Schiegg and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Variational Message passing written by Andrew John Frank and published by . This book was released on 2013 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on both the application and development of variational inference algorithms for probabilistic graphical models. First, we propose a new application of graphical models and approximate inference in the multi-target tracking domain. By constructing a factor graph representation of the track-oriented multiple hypothesis tracker, we enable the application of variational inference algorithms to efficiently estimate marginal probabilities of possible tracks. We then show that that these track marginals are the key ingredient in a multi-target generalization of the standard expectation-maximization algorithm used for parameter estimation in single-target tracking. The resulting online estimation algorithm makes the tracker robust to parameter misspecification and can improve performance in settings with non-stationary target dynamics. Next, we develop a general framework to extend algorithms for approximate marginalization in discrete systems to work with continuous-valued graphical models. We extend the particle belief propagation algorithm, which uses importance sampling to lift the sum and product operations of belief propagation from a variable's continuous domain into an importance-reweighted particle domain. We demonstrate that this framework admits other variational inference algorithms such as mean field and tree-reweighted belief propagation, and that they confer similar qualitative benefits to continuous-valued models as in the discrete domain.
Download or read book Multi sensor Target Tracking written by Jun Ye Yu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Target tracking is a well-studied research topic with a vast array of applications. The basic idea is to track one or more targets of interest using data collected by one or more sensors. While a single sensor may provide enough data, it is more beneficial to establish a network of sensors that collaborate with each other. In this thesis, we study multi-sensor target tracking and present three manuscripts.In the first manuscript, we present a distributed bearings-only single-target particle filter. Unlike the existing literature, the proposed filter incorporates the Earth's curvature in the measurement model to provide more accurate bearing computation. Furthermore, we derive an approximate joint log-likelihood function to reduce the total communication overhead. In the second manuscript, we extend our work in the first manuscript and present two compression algorithms for distributed particle filters. The proposed algorithms construct a graph over the particles and exploit the resulting graph Laplacian matrix to encode the particle log-likelihoods. The proposed algorithms are not limited to any measurement models and can be incorporated in any generic particle filter. We also derive theoretical results showing that the proposed algorithms outperform existing methods at low communication overhead. In the third manuscript, we study data assignment in multi-target tracking. We propose two heuristic but computationally efficient algorithms for multi-sensor multi-target data assignment that can generate a number of likely target-measurement associations. We also implement these algorithms in a generalized labeled multi-Bernoulli filter to validate their performance." --
Download or read book Particle Filters for Random Set Models written by Branko Ristic and published by Springer Science & Business Media. This book was released on 2013-04-15 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.
Download or read book On Computationally Efficient Frameworks for Data Association in Multi target Tracking written by Sriram Krishnaswamy (Ph. D. in mechanical engineering) and published by . This book was released on 2019 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this dissertation is to examine ways of improving the computational efficiency of data association algorithms in tracking and to do so with better methods to handle data. Data association algorithms are employed in tracking problems in conjunction with an estimation algorithm to determine the optimal state estimate of multiple objects of interest given a set of measurements. This work primarily deals with Bayesian or pseudo-Bayesian paradigms for data association and reduces the computational cost by reducing the exponential growth or the so-called "curse of dimensionality'' in these problems. This increase in the number of hypotheses is exacerbated in dense environments with low signal-to-noise ratio (SNR). This research employs tensor decomposition to reduce the number of incoming measurements into a core tensor or a low-dimensional summary and use it as a substitute for the complete set of measurements.
Download or read book Random Finite Sets for Robot Mapping SLAM written by John Stephen Mullane and published by Springer Science & Business Media. This book was released on 2011-05-19 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: The monograph written by John Mullane, Ba-Ngu Vo, Martin Adams and Ba-Tuong Vo is devoted to the field of autonomous robot systems, which have been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the problem of representing the environment and its uncertainty in terms of feature based maps. Random Finite Sets are adopted as the fundamental tool to represent a map, and a general framework is proposed for feature management, data association and state estimation. The approaches are tested in a number of experiments on both ground based and marine based facilities.
Download or read book Multiple target Tracking in Complex Scenarios written by Srinivas Phani Kumar Chavali and published by . This book was released on 2013 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we develop computationally efficient algorithms for multiple-target tracking (MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm (and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter (MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound (PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter (HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection (SS), resource allocation (RA) and data fusion (DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently.
Download or read book Computer Vision ECCV 2020 Workshops written by Adrien Bartoli and published by Springer Nature. This book was released on 2021-01-02 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 6-volume set, comprising the LNCS books 12535 until 12540, constitutes the refereed proceedings of 28 out of the 45 workshops held at the 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic. The 249 full papers, 18 short papers, and 21 further contributions included in the workshop proceedings were carefully reviewed and selected from a total of 467 submissions. The papers deal with diverse computer vision topics. Part IV focusses on advances in image manipulation; assistive computer vision and robotics; and computer vision for UAVs.
Download or read book Target Tracking with Random Finite Sets written by Weihua Wu and published by Springer Nature. This book was released on 2023-08-02 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.
Download or read book Efficient Approaches for Report Cluster Correlation in Multitarget Tracking Systems written by and published by . This book was released on 1990 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gating is an important component of most multi-object tracking systems. Its function is to identify sensor reports, e.g., radar or infrared (IR) returns from missiles, planes, etc., that correlate highly with current state estimates (i.e., tracks). For small numbers of objects, it is feasible to calculate a probability of correlation for every track/report pair and reject those whose probabilities fall below some threshold. For large numbers of objects, however, the quadratic growth in the number of pairs for which correlation probabilities are computed by this 'brute force' approach represents an enormous bottleneck. This combination problem is of particular concern in Strategic Defense Initiative (SDI) tracking and correlation for which numbers of objects on the order of 100,000 must be processed in real time. This report discusses an approach that significantly reduces the computational complexity of the correlation process in the TRC tracking and correlation system developed at the Naval Research Laboratory. TRC is a multihypothesis tracker/correlator that was developed to conduct experiments in multiple-target tracking. Unfortunately, early tests of the TRC revealed that combinatorial problems severely limited the size of the scenarios that could be examined. Subsequent analysis demonstrated that these limitations were the result of a correlation (gating) algorithm that scaled in time quadratically in the size of the scenarios that could be examined. Subsequent analysis demonstrated that these limitations were the result of a correlation (gating) algorithm that scaled in time quadratically in the size of the scenario.
Download or read book Methods and Applications for Modeling and Simulation of Complex Systems written by Fazilah Hassan and published by Springer Nature. This book was released on 2023-11-13 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 22nd Asia Simulation Conference on Methods and Applications for Modeling and Simulation of Complex Systems, AsiaSim 2023, held in Langkawi, Malaysia, during October 25–26, 2023. The 77 full papers included in this book were carefully reviewed and selected from 164 submissions. They were organized in topical sections as follows: Modelling and Simulation, Artificial intelligence, Industry 4.0, Digital Twins Modelling, Simulation and Gaming, Simulation for Engineering, Simulation for Sustainable Development, Simulation in Social Sciences.
Download or read book Multi Camera Networks written by Hamid Aghajan and published by Academic Press. This book was released on 2009-04-25 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: - The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring - Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications - Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware This book is the definitive reference in multi-camera networks. It gives clear guidance on the conceptual and implementation issues involved in the design and operation of multi-camera networks, as well as presenting the state-of-the-art in hardware, algorithms and system development. The book is broad in scope, covering smart camera architectures, embedded processing, sensor fusion and middleware, calibration and topology, network-based detection and tracking, and applications in distributed and collaborative methods in camera networks. This book will be an ideal reference for university researchers, R&D engineers, computer engineers, and graduate students working in signal and video processing, computer vision, and sensor networks. Hamid Aghajan is a Professor of Electrical Engineering (consulting) at Stanford University. His research is on multi-camera networks for smart environments with application to smart homes, assisted living and well being, meeting rooms, and avatar-based communication and social interactions. He is Editor-in-Chief of Journal of Ambient Intelligence and Smart Environments, and was general chair of ACM/IEEE ICDSC 2008. Andrea Cavallaro is Reader (Associate Professor) at Queen Mary, University of London (QMUL). His research is on target tracking and audiovisual content analysis for advanced surveillance and multi-sensor systems. He serves as Associate Editor of the IEEE Signal Processing Magazine and the IEEE Trans. on Multimedia, and has been general chair of IEEE AVSS 2007, ACM/IEEE ICDSC 2009 and BMVC 2009. - The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring - Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications - Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware
Download or read book Computer Vision ECCV 2016 written by Bastian Leibe and published by Springer. This book was released on 2016-09-16 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.
Download or read book Remote Sensing in Vessel Detection and Navigation written by Henning Heiselberg and published by MDPI. This book was released on 2020-12-11 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Special Issue entitled “Remote Sensing in Vessel Detection and Navigation” comprises 15 articles on many topics related to remote sensing with navigational sensors. The sequence of articles included in this Special Issue is in line with the latest scientific trends. The latest developments in science, including artificial intelligence, were used. It can be said that navigation and vessel detection remain important and hot topics, and a lot of work will continue to be done worldwide. New techniques and methods for analyzing and extracting information from navigational sensors and data have been proposed and verified. Some of these will spark further research, and some are already mature and can be considered for industrial implementation and development.
Download or read book Model based Visual Tracking written by Giorgio Panin and published by John Wiley & Sons. This book was released on 2011-04-12 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has two main goals: to provide a unifed and structured overview of this growing field, as well as to propose a corresponding software framework, the OpenTL library, developed by the author and his working group at TUM-Informatik. The main objective of this work is to show, how most real-world application scenarios can be naturally cast into a common description vocabulary, and therefore implemented and tested in a fully modular and scalable way, through the defnition of a layered, object-oriented software architecture.The resulting architecture covers in a seamless way all processing levels, from raw data acquisition up to model-based object detection and sequential localization, and defines, at the application level, what we call the tracking pipeline. Within this framework, extensive use of graphics hardware (GPU computing) as well as distributed processing, allows real-time performances for complex models and sensory systems.
Download or read book Proceedings of the Eleventh National Conference on Communications written by and published by Allied Publishers. This book was released on 2005 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt:
