Extended Object Tracking with Random Hypersurface Models
Marcus Baum,Uwe D. Hanebeck +1 more
Reads0
Chats0
TLDR
In this paper, the random hypersurface model (RHM) is introduced for estimating a shape approximation of an extended object in addition to its kinematic state, where the shape parameters and measurements are related via a measurement equation that serves as the basis for a Gaussian state estimator.Abstract:
The random hypersurface model (RHM) is introduced for estimating a shape approximation of an extended object in addition to its kinematic state. An RHM represents the spatial extent by means of randomly scaled versions of the shape boundary. In doing so, the shape parameters and the measurements are related via a measurement equation that serves as the basis for a Gaussian state estimator. Specific estimators are derived for elliptic and star-convex shapes.read more
Citations
More filters
Journal ArticleDOI
Extended Target Tracking Using Gaussian Processes
Niklas Wahlström,Emre Ozkan +1 more
TL;DR: This paper proposes using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan, that creates a model that describes the shape and the kinematics of the object.
Proceedings ArticleDOI
A learning approach for real-time temporal scene flow estimation from LIDAR data
TL;DR: A novel algorithm is presented that estimates motion from raw LIDAR data in real-time without the need for segmentation or model-based tracking and is evaluated on the KITTI dataset.
Journal ArticleDOI
Recursive Bayesian filtering in circular state spaces
TL;DR: This work introduces a general framework for estimation of a circular state based on different circular distributions, specifically the wrapped normal (WN) distribution and the von Mises distribution, and proposes an estimation method for circular systems with nonlinear system and measurement functions.
Proceedings Article
Multiple extended object tracking using Gaussian processes
TL;DR: The ability of Gaussian processes to estimate and represent a wide range of free-from shapes is combined with a principled approach to the multiple extended objects tracking problem and a new, approximately axis-symmetric covariance function is additionally introduced.
Journal ArticleDOI
Joint Probabilistic Data Association Tracker for Extended Target Tracking Applied to X-Band Marine Radar Data
Gemine Vivone,Paolo Braca +1 more
TL;DR: This paper proposes a signal processing chain composed by a detector and a joint probabilistic data association (JPDA) tracker to handle the problem of multiple ETT and to jointly estimate both the targets' kinematics and their sizes, i.e., length and width.
References
More filters
Journal ArticleDOI
Unscented filtering and nonlinear estimation
Simon Julier,Jeffrey Uhlmann +1 more
TL;DR: The motivation, development, use, and implications of the UT are reviewed, which show it to be more accurate, easier to implement, and uses the same order of calculations as linearization.
Journal ArticleDOI
Bayesian approach to extended object and cluster tracking using random matrices
TL;DR: With simulated sensor data produced by a partly unresolvable aircraft formation the addressed phenomena are illustrated and an approximate Bayesian solution to the resulting tracking problem is proposed.
Journal ArticleDOI
Tracking of Extended Objects and Group Targets Using Random Matrices
TL;DR: In this article, the problem of maintaining a track for an extended object or group target with varying number of detections was analyzed and discussed, and a new approach was derived that is expected to overcome some of the weaknesses the mentioned Bayesian approach suffers from in certain applications.
Journal ArticleDOI
Motion estimation via dynamic vision
TL;DR: The authors formulate the visual motion estimation problem in terms of identification of nonlinear implicit systems with parameters on a topological manifold and propose a dynamic solution either in the local coordinates or in the embedding space of the parameter manifold.
Journal ArticleDOI
Spatial distribution model for tracking extended objects
K. Gilholm,D. Salmond +1 more
TL;DR: In this paper, a Bayesian filter was developed for tracking an extended object in clutter based on two simple axioms: (i) the number of received target and clutter measurements in a frame are Poisson distributed (so several measurements may originate from the target) and (ii) target extent is modelled by a spatial probability distribution and each targetrelated measurement is an independent 'random draw' from this spatial distribution (convolved with a sensor model).