Search

Extended objects generate a variable number of multiple measurements. In contrast with point targets, extended objects are characterized with their size or volume, and orientation. Multiple object tracking is a notoriously challenging problem due to complexities caused by data association. This paper develops a box particle filter (box PF) method for multiple extended object tracking, and for the first time, it is shown how interval-based approaches can deal efficiently with data association problems and reduce the computational complexity of the data association. The box PF relies on the concept of a box particle. A box particle represents a random sample and occupies a controllable rectangular region of nonzero volume in the object state space. A theoretical proof of the generalized likelihood of the box PF for multiple extended objects is given based on a binomial expansion. Next, the performance of the box PF is evaluated using a challenging experiment with the appearance and disappearance of objects within the area of interest, with real laser rangefinder data. The box PF is compared with a state-of-the-art particle filter with point particles. Accurate and robust estimates are obtained with the box PF, both for the kinematic states and extent parameters, with significant reductions in computational complexity. The box PF reduction of computational time is atleast 32% compared with the particle filter working with point particles for the experiment presented. Another advantage of the box PF is its robustness to initialization uncertainty.

This work presents sequential Bayesian detection and estimation methods for nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. Following Mahler’s framework for information fusion, the paper develops the optimal Bayes filter for this problem in the form of the Bernoulli filter for interval measurements. Two numerical implementations of the optimal filter are developed. The first is the Bernoulli particle filter (PF), which turns out to require a large number of particles in order to achieve a satisfactory performance. For the sake of reduction in the number of particles, the paper also develops an implementation based on box particles, referred to as the Bernoulli Box-PF. A box particle is a random sample that occupies a small and controllable rectangular region of non-zero volume in the target state space. Manipulation of boxes utilizes the methods of interval analysis. The two implementations are compared numerically and found to perform remarkably well: the target is reliably detected and the posterior probability density function of the target state is estimated accurately. The Bernoulli Box-PF, however, when designed carefully, is computationally more efficient.

The development of real-time traffic models is of paramount importance for the purposes of optimizing traffic flow. Inspired by the compositional model (CM) and the METANET model, this paper proposes an interval approach for macroscopic traffic modeling. We develop an interval CM (ICM) and an interval implementation of the METANET model (IMETANET) that provide a natural way of predicting traffic flows without the assumption of uniform distribution of vehicles in a cell. The interval macroscopic models are suitable for real-time applications in road networks and can be part of road traffic surveillance and control systems. The performances of the interval approaches are investigated for both the ICM and the IMETANET models. The efficiency of the interval models is demonstrated over simulated data, and as well as over real traffic data from Motorway Incident Detection and Automatic Signalling (MIDAS) data sets from the United Kingdom.

This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure evolving models are proposed for automatic graph structure initialization, incorporation of new nodes, unexisting nodes removal, and the edge update. Both the state and the graph structure are updated based on range and bearing measurements. This evolving graph model is propagated combined with a sequential Monte Carlo framework able to cope with measurement origin uncertainty. The effectiveness of the proposed approach is illustrated over scenarios for group motion estimation in urban environments. Results with challenging scenarios with merging, splitting, and crossing of groups are presented with high estimation accuracy. The performance of the algorithm is also evaluated and shown on real ground moving target indicator (GMTI) radar data and in the presence of data origin uncertainty.

International audience; We consider the problem of localising an unknown number of land mines usingconcentration information provided by a wireless sensor network. A number of vapoursensors/detectors, deployed in the region of interest, are able to detect the concentrationof the explosive vapours, emanating from buried land mines. The collected data iscommunicated to a fusion centre. Using a model for the transport of the explosive chemicalsin the air, we determine the unknown number of sources using a Principal ComponentAnalysis (PCA)-based technique. We also formulate the inverse problem of determiningthe positions and emission rates of the land mines using concentration measurementsprovided by the wireless sensor network. We present a solution for this problem basedon a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme,and we compare it to the least squares optimisation approach. Experiments conducted onsimulated data show the effectiveness of the proposed approach.

Through its ability to create situation awareness, multi-target target tracking is an extremely important capability for almost any kind of surveillance and tracking system. Many approaches have been proposed to address its inherent challenges. However, the majority of these approaches make two assumptions: the probability of detection and the clutter rate are constant. However, neither are likely to be true in practice. For example, as the projected size of a target becomes smaller as it moves further from the sensor, the probability of detection will decline. When target detection is carried out using templates, clutter rate will depend on how much the environment resembles the current target of interest. In this paper, we begin to investigate the impacts on these effects. Using a simulation environment inspired by the challenges of Wide Area Surveillance (WAS), we develop a state dependent formulation for probability of detection and clutter. The impacts of these models are compared in a simulated urban environment populated by multiple vehicles and cursed with occlusions. The results show that accurate modelling the effects of occlusion and degradation in detection, significant improvements in performance can be obtained.

The objective of the project reported here was to develop, implement and evaluate a model of the probability of detection of moving objects in Wide Area Motion Imagery (WAMI) that incorporates the effects of the target, the platform, and the environment. Developing situation awareness is vital for almost any kind of military operation. Through understanding the state and nature of the environment, military personnel can plan and respond accordingly. Situation awareness is often treated as the problem of knowing where all the potential targets are. Through knowing the locations of these targets, threats can be identified and countered. Another important source of awareness is to understand where targets cannot be. Regions that are free of targets can be used to constrain where targets might be. To meet these needs, Wide Area Surveillance (WAS) systems have been developed that are able to sense large swaths of an environment simultaneously and at high resolution. However, the next key challenge is to automatically analyze this image data to, for example, track the locations of targets and identify potential anomalous behavior. This report begins to explore how the output from a WAS system can be used by a state-of-the-art multi-target tracker. In particular, we considered how the output of the image processing and matching algorithms used in the Likelihood of Features Tracker (LoFT) could be combined with a Probabilistic Hypothesis Density (PHD) Filter. Using machine learning techniques, we have developed a formalism and algorithms to automatically predict how the visual appearance of a vehicle can change over time. Using this prediction model, we are then able to automatically threshold and detect potential candidate vehicle locations, and assess both probability of detection and the probability of clutter.

This chapter presents a new approach combining the Bayesian framework with interval methods. When the system dynamics and measurement models have interval types of uncertainties, instead of point state estimates, guaranteed (interval) estimation is a promising approach. First, fundamental concepts from the interval analysis are introduced. Next, a Box Particle Filter (Box-PF) is presented and its theoretical derivation is given based on a mixture of uniform probability density functions. The efficiency of the Box-PF is significant compared with the generic sampling importance resampling particle Filter (SIR PF). With few particles the Box-PF can achieve the same estimation accuracy that the SIR PF achieves with thousands of particles. The performance of the proposed Box-PF is studied and results over examples both with simulated and real data are presented.

Multitarget tracking is fundamental in many security and surveillance applications. However, as algorithms have been applied in more and more challenging environments, traditional simplifications no longer even approximately hold true. In particular, we consider the problem of maritime surveillance in which small targets (boats) are to be detected and tracked in the presence of highly structured noise (waves). In particular, we consider two problems. The first is that, given the resolution of modern radar systems, even small targets are extended, straddling multiple range or azimuth bins. The second is that sea clutter is not a uniform, Poisson-distributed noise process but is highly spatially varying. In this paper, we develop two extensions of the Probability Hypothesis Density (PHD) Filter. Using a generalised likelihood model, extended targets can be readily accounted for. Through the use of spatially varying clutter models, structured noise approximation is provided. The algorithms were developed and tested using a sea trial in which Rigid-Hulled Inflatable Boats (RHIBs), equipped with GPS receivers, were tracked using a radar system.

We present algorithms for tracking and reasoning of local traits in the subsystem level based on the observed emergent behavior of multiple coordinated groups in potentially cluttered environments. Our proposed Bayesian inference schemes, which are primarily based on (Markov chain) Monte Carlo sequential methods, include: 1) an evolving network-based multiple object tracking algorithm that is capable of categorizing objects into groups, 2) a multiple cluster tracking algorithm for dealing with prohibitively large number of objects, and 3) a causality inference framework for identifying dominant agents based exclusively on their observed trajectories.We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are provided for demonstrating the derived concepts.

This paper proposes a sequential Monte Carlo filter (particle filter) for state and parameter estimation of dynamic systems. It is applied to the problem of extended object tracking in the presence of dense clutter. The unknown length of a stick-shape object is estimated in addition to the kinematic parameters. The kernel density estimation technique is utilised to approximate the joint posterior density of target state and static size parameters. The convolution particle filtering approach is validated on a Poisson model for the measurements, originating from the target and clutter. Examples illustrating the filter performance are presented. Simulation results show that the convolution particle filter provides accurate on-line tracking, with very good estimates both for the target kinematic states and for the parameters of the target extent.

This paper considers the challenging task of realtime extended object tracking using cluttered measurements from laser range scanners. The performance of the recently proposed Box Particle Filter (Box PF) algorithm is evaluated utilising real measurements from laser range scanners obtained within a prototype security system replicating an airport corridor. The problem is expressed as the joint estimation of both state and parameters of an extended target. Circularly and elliptically shaped targets are considered. Promising results are presented.