Box-Particle Labeled Multi-Bernoulli Filter for Multiple Extended Target Tracking

This paper focuses on real-time tracking of multiple extended targets in clutter based on labeled multi-Bernoulli filter. To address this problem, a novel approach is proposed within the recently presented box-particle framework. Unlike the traditional point-particle approach, the measurements of extended targets are modeled as interval measurements in this work, and the corresponding likelihood function is given based on interval analysis. Then, labeled multi-Bernoulli recursion for extended targets is implemented by box particles, referred to as BP-LMB filter. Furthermore, BP-MM-LMB filter is proposed to better accommodate the uncertainty of target dynamics by integrating the BP-LMB filter with interacting multiple models (IMM) algorithm. Simulations demonstrate that the proposed approach can significantly reduce the number of particles and well track multiple extended targets with less runtime

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach

The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources

Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios

Multi-target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. Besides, the key problem of data association needs to be handled effectively considering the limitations in the computational resources on-board an autonomous car. The challenge of the tracking problem is further evident in the use of high-resolution automotive sensors which return multiple detections per object. Furthermore, it is customary to use multiple sensors that cover different and/or over-lapping Field of View and fuse sensor detections to provide robust and reliable tracking. As a consequence, in high-resolution multi-sensor settings, the data association uncertainty, and the corresponding tracking complexity increases pointing to a systematic approach to handle and process sensor detections. In this work, we present a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management features. These tracking functionalities can help facilitate perception during common events in public traffic as participants (suddenly) change lanes, navigate intersections, overtake and/or brake in emergencies, etc. Various tracking approaches including the ones based on joint integrated probability data association (JIPDA) filter, Linear Multi-target Integrated Probabilistic Data Association (LMIPDA) Filter, and their multi-detection variants are adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The utility of the filtering module is further elaborated by integrating it into a trajectory tracking problem based on model predictive control. To cope with tracking complexity in the case of multiple high-resolution sensors, we propose a hybrid scheme that combines the approaches of data clustering at the local sensor and multiple detections tracking schemes at the fusion layer. We implement a track-to-track fusion scheme that de-correlates local (sensor) tracks to avoid double counting and apply a measurement partitioning scheme to re-purpose the LMIPDA tracking algorithm to multi-detection cases. In addition to the measurement partitioning approach, a joint extent and kinematic state estimation scheme are integrated into the LMIPDA approach to facilitate perception and tracking of an individual as well as group targets as applied to multi-lane public traffic. We formulate the tracking problem as a two hierarchical layer. This arrangement enhances the multi-target tracking performance in situations including but not limited to target initialization(birth process), target occlusion, missed detections, unresolved measurement, target maneuver, etc. Also, target groups expose complex individual target interactions to help in situation assessment which is challenging to capture otherwise. The simulation studies are complemented by experimental studies performed on single and multiple (group) targets. Target detections are collected from a high-resolution radar at a frequency of 20Hz; whereas RTK-GPS data is made available as ground truth for one of the target vehicle\u27s trajectory

Bayesian multi-target tracking: application to total internal reflection fluorescence microscopy

This thesis focuses on the problem of automated tracking of tiny cellular and sub-cellular structures, known as particles, in the sequences acquired from total internal reflection fluorescence microscopy (TIRFM) imaging technique. Our primary biological motivation is to develop an automated system for tracking the sub-cellular structures involving exocytosis (an intracellular mechanism) which is helpful for studying the possible causes of the defects in diseases such as diabetes and obesity. However, all methods proposed in this thesis are generalized to be applicable for a wide range of particle tracking applications. A reliable multi-particle tracking method should be capable of tracking numerous similar objects in the presence of high levels of noise, high target density and complex motions and interactions. In this thesis, we choose the Bayesian filtering framework as our main approach to deal with this problem. We focus on the approaches that work based on detections. Therefore, in this thesis, we first propose a method that robustly detects the particles in the noisy TIRFM sequences with inhomogeneous and time-varying background. In order to evaluate our detection and tracking methods on the sequences with known and reliable ground truth, we also present a framework for generating realistic synthetic TIRFM data. To propose a reliable multi-particle tracking method for TIRFM sequences, we suggest a framework by combining two robust Bayesian filters, the interacting multiple model and joint probabilistic data association (IMM-JPDA) filters. The performance of our particle tracking method is compared against those of several popular and state-of-the art particle tracking approaches on both synthetic and real sequences. Although our approach performs well in tracking particles, it can be very computationally demanding for the applications with dense targets with poor detections. To propose a computationally cheap, but reliable, multi-particle tracking method, we investigate the performance of a recent multi-target Bayesian filter based on random finite theory, the probability hypothesis density (PHD) filter, on our application. To this end, we propose a general framework for tracking particles using this filter. Moreover, we assess the performance of our proposed PHD filter on both synthetic and real sequences with high level of noise and particle density. We compare its results from both aspects of accuracy and processing time against our IMM-JPDA filter. Finally, we suggest a framework for tracking particles in a challenging problem where the noise characteristic and the background intensity of sequences change during the acquisition process which make detection profile and clutter rate time-variant. To deal with this, we propose a bootstrap filter using another type of the random finite set based Bayesian filters, the cardinalized PHD (CPHD) filter, composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability while the tracker estimates the state of the targets. We evaluate the performance of our bootstrap on both synthetic and real sequences under these time-varying conditions. Moreover, its performance is compared against those of our other particle trackers as well as the state-of-the art particle tracking approaches

Random finite sets in multi-target tracking - efficient sequential MCMC implementation

Over the last few decades multi-target tracking (MTT) has proved to be a challenging and attractive research topic. MTT applications span a wide variety of disciplines, including robotics, radar/sonar surveillance, computer vision and biomedical research. The primary focus of this dissertation is to develop an effective and efficient multi-target tracking algorithm dealing with an unknown and time-varying number of targets. The emerging and promising Random Finite Set (RFS) framework provides a rigorous foundation for optimal Bayes multi-target tracking. In contrast to traditional approaches, the collection of individual targets is treated as a set-valued state. The intent of this dissertation is two-fold; first to assert that the RFS framework not only is a natural, elegant and rigorous foundation, but also leads to practical, efficient and reliable algorithms for Bayesian multi-target tracking, and second to provide several novel RFS based tracking algorithms suitable for the specific Track-Before-Detect (TBD) surveillance application. One main contribution of this dissertation is a rigorous derivation and practical implementation of a novel algorithm well suited to deal with multi-target tracking problems for a given cardinality. The proposed Interacting Population-based MCMC-PF algorithm makes use of several Metropolis-Hastings samplers running in parallel, which interact through genetic variation. Another key contribution concerns the design and implementation of two novel algorithms to handle a varying number of targets. The first approach exploits Reversible Jumps. The second approach is built upon the concepts of labeled RFSs and multiple cardinality hypotheses. The performance of the proposed algorithms is also demonstrated in practical scenarios, and shown to significantly outperform conventional multi-target PF in terms of track accuracy and consistency. The final contribution seeks to exploit external information to increase the performance of the surveillance system. In multi-target scenarios, kinematic constraints from the interaction of targets with their environment or other targets can restrict target motion. Such motion constraint information is integrated by using a fixed-lag smoothing procedure, named Knowledge-Based Fixed-Lag Smoother (KB-Smoother). The proposed combination IP-MCMC-PF/KB-Smoother yields enhanced tracking

Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced. © 2013 Elsevier Inc. All rights reserved

Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing

We address the challenge of tracking an unknown number of targets in strong clutter environments using measurements from a radar sensor. Leveraging the range-Doppler spectra information, we identify the measurement classes, which serve as additional information to enhance clutter rejection and data association, thus bolstering the robustness of target tracking. We first introduce a novel neural enhanced message passing approach, where the beliefs obtained by the unified message passing are fed into the neural network as additional information. The output beliefs are then utilized to refine the original beliefs. Then, we propose a classification-aided robust multiple target tracking algorithm, employing the neural enhanced message passing technique. This algorithm is comprised of three modules: a message-passing module, a neural network module, and a Dempster-Shafer module. The message-passing module is used to represent the statistical model by the factor graph and infers target kinematic states, visibility states, and data associations based on the spatial measurement information. The neural network module is employed to extract features from range-Doppler spectra and derive beliefs on whether a measurement is target-generated or clutter-generated. The Dempster-Shafer module is used to fuse the beliefs obtained from both the factor graph and the neural network. As a result, our proposed algorithm adopts a model-and-data-driven framework, effectively enhancing clutter suppression and data association, leading to significant improvements in multiple target tracking performance. We validate the effectiveness of our approach using both simulated and real data scenarios, demonstrating its capability to handle challenging tracking scenarios in practical radar applications.Comment: 15 page

Multi-Robot Active Information Gathering Using Random Finite Sets

Many tasks in the modern world involve collecting information, such as infrastructure inspection, security and surveillance, environmental monitoring, and search and rescue. All of these tasks involve searching an environment to detect, localize, and track objects of interest, such as damage to roadways, suspicious packages, plant species, or victims of a natural disaster. In any of these tasks the number of objects of interest is often not known at the onset of exploration. Teams of robots can automate these often dull, dirty, or dangerous tasks to decrease costs and improve speed and safety. This dissertation addresses the problem of automating data collection processes, so that a team of mobile sensor platforms is able to explore an environment to determine the number of objects of interest and their locations. In real-world scenarios, robots may fail to detect objects within the field of view, receive false positive measurements to clutter objects, and be unable to disambiguate true objects. This makes data association, i.e., matching individual measurements to targets, difficult. To account for this, we utilize filtering algorithms based on random finite sets to simultaneously estimate the number of objects and their locations within the environment without the need to explicitly consider data association. Using the resulting estimates they receive, robots choose actions that maximize the mutual information between the set of targets and the binary events of receiving no detections. This effectively hedges against uninformative actions and leads to a closed form equation to compute mutual information, allowing the robot team to plan over a long time horizon. The robots either communicate with a central agent, which performs the estimation and control computations, or act in a decentralized manner. Our extensive hardware and simulated experiments validate the unified estimation and control framework, using robots with a wide variety of mobility and sensing capabilities to showcase the broad applicability of the framework

Single to multiple target, multiple type visual tracking

Visual tracking is a key task in applications such as intelligent surveillance, humancomputer interaction (HCI), human-robot interaction (HRI), augmented reality (AR), driver assistance systems, and medical applications. In this thesis, we make three main novel contributions for target tracking in video sequences. First, we develop a long-term model-free single target tracking by learning discriminative correlation filters and an online classifier that can track a target of interest in both sparse and crowded scenes. In this case, we learn two different correlation filters, translation and scale correlation filters, using different visual features. We also include a re-detection module that can re-initialize the tracker in case of tracking failures due to long-term occlusions. Second, a multiple target, multiple type filtering algorithm is developed using Random Finite Set (RFS) theory. In particular, we extend the standard Probability Hypothesis Density (PHD) filter for multiple type of targets, each with distinct detection properties, to develop multiple target, multiple type filtering, N-type PHD filter, where N ≥ 2, for handling confusions that can occur among target types at the measurements level. This method takes into account not only background false positives (clutter), but also confusions between target detections, which are in general different in character from background clutter. Then, under the assumptions of Gaussianity and linearity, we extend Gaussian mixture (GM) implementation of the standard PHD filter for the proposed N-type PHD filter termed as N-type GM-PHD filter. Third, we apply this N-type GM-PHD filter to real video sequences by integrating object detectors’ information into this filter for two scenarios. In the first scenario, a tri-GM-PHD filter is applied to real video sequences containing three types of multiple targets in the same scene, two football teams and a referee, using separate but confused detections. In the second scenario, we use a dual GM-PHD filter for tracking pedestrians and vehicles in the same scene handling their detectors’ confusions. For both cases, Munkres’s variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames. We make extensive evaluations of these developed algorithms and find out that our methods outperform their corresponding state-of-the-art approaches by a large margin.EPSR