6,277 research outputs found
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
Monte-Carlo Methods and the Step-Back Kalman Filter for Orbital State Estimation
This research assesses the performance of filtering schemes for tracking uncooperative satellites through space-based optical measurements, and identifies a simple and numerically stable methodology that ameliorates the poor performance of standard filtering schemes at a substantially reduced cost in comparison to nonlinear particle filter-based remedies. Traditional filtering schemes, such as the extended Kalman filter (EKF) and unscented Kalman filter (UKF), both diverge when tracking a resident space object (RSO) in geosynchronous orbit (GEO) when there is a long time duration between measurements. This divergence is identified as a consequence of nonlinearity in the dynamics and nonlinearity in the optical measurements, both of which cause the underlying density of the state to deviate from a Gaussian distribution. A Gaussian sum filter based on using a Gaussian mixture model (GMM) for the probability density function can be implemented in order to avoid this divergence, but this comes at a high computational cost and has numerical sensitivity problems under reasonable orbital conditions. An alternative filter algorithm has been developed, referred to as the extended step-back Kalman filter (ESBKF), which is shown to effectively track the RSO in GEO while avoiding the computational burden and numerical sensitivity of the GMM filter. This filter applies the measurement updates to statistics at a time in the past when the distribution was approximately Gaussian, and then propagates the updated statistics forward to the present. In this manuscript the mathematical structure and properties of the ESBKF are presented, and its utility is demonstrated on tracking an RSO in a GEO orbit with right-ascension and declination angle measurements from an observer satellite
The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker
Reliable collision avoidance is one of the main requirements for autonomous
driving. Hence, it is important to correctly estimate the states of an unknown
number of static and dynamic objects in real-time. Here, data association is a
major challenge for every multi-target tracker. We propose a novel multi-target
tracker called Greedy Dirichlet Process Filter (GDPF) based on the
non-parametric Bayesian model called Dirichlet Processes and the fast posterior
computation algorithm Sequential Updating and Greedy Search (SUGS). By adding a
temporal dependence we get a real-time capable tracking framework without the
need of a previous clustering or data association step. Real-world tests show
that GDPF outperforms other multi-target tracker in terms of accuracy and
stability
Multi-Object Tracking with Interacting Vehicles and Road Map Information
In many applications, tracking of multiple objects is crucial for a
perception of the current environment. Most of the present multi-object
tracking algorithms assume that objects move independently regarding other
dynamic objects as well as the static environment. Since in many traffic
situations objects interact with each other and in addition there are
restrictions due to drivable areas, the assumption of an independent object
motion is not fulfilled. This paper proposes an approach adapting a
multi-object tracking system to model interaction between vehicles, and the
current road geometry. Therefore, the prediction step of a Labeled
Multi-Bernoulli filter is extended to facilitate modeling interaction between
objects using the Intelligent Driver Model. Furthermore, to consider road map
information, an approximation of a highly precise road map is used. The results
show that in scenarios where the assumption of a standard motion model is
violated, the tracking system adapted with the proposed method achieves higher
accuracy and robustness in its track estimations
Vehicle detection and tracking using homography-based plane rectification and particle filtering
This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results
Single camera pose estimation using Bayesian filtering and Kinect motion priors
Traditional approaches to upper body pose estimation using monocular vision
rely on complex body models and a large variety of geometric constraints. We
argue that this is not ideal and somewhat inelegant as it results in large
processing burdens, and instead attempt to incorporate these constraints
through priors obtained directly from training data. A prior distribution
covering the probability of a human pose occurring is used to incorporate
likely human poses. This distribution is obtained offline, by fitting a
Gaussian mixture model to a large dataset of recorded human body poses, tracked
using a Kinect sensor. We combine this prior information with a random walk
transition model to obtain an upper body model, suitable for use within a
recursive Bayesian filtering framework. Our model can be viewed as a mixture of
discrete Ornstein-Uhlenbeck processes, in that states behave as random walks,
but drift towards a set of typically observed poses. This model is combined
with measurements of the human head and hand positions, using recursive
Bayesian estimation to incorporate temporal information. Measurements are
obtained using face detection and a simple skin colour hand detector, trained
using the detected face. The suggested model is designed with analytical
tractability in mind and we show that the pose tracking can be
Rao-Blackwellised using the mixture Kalman filter, allowing for computational
efficiency while still incorporating bio-mechanical properties of the upper
body. In addition, the use of the proposed upper body model allows reliable
three-dimensional pose estimates to be obtained indirectly for a number of
joints that are often difficult to detect using traditional object recognition
strategies. Comparisons with Kinect sensor results and the state of the art in
2D pose estimation highlight the efficacy of the proposed approach.Comment: 25 pages, Technical report, related to Burke and Lasenby, AMDO 2014
conference paper. Code sample: https://github.com/mgb45/SignerBodyPose Video:
https://www.youtube.com/watch?v=dJMTSo7-uF
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