The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This study explores the non-parametric estimation of a shape boundary from noisy points in 2D when the sensor characteristics are known. As the underlying shape information is not known, the offered algorithm estimates points on the shape boundary by using the statistics of the subsets of point cloud data. The novel approach proposed in this paper is able to find corner points in a local geometry...
This paper presents a novel approach to track a nonconvex shape approximation of an extended target based on noisy point measurements. For this purpose, a novel type of random hypersurface model (RHM) called Level-set RHM is introduced that models the interior of a shape with level-sets of an implicit function. Based on the Level-set RHM, a nonlinear measurement equation can be derived that allows...
In this paper, we present a novel online approach for tracking whole-body human motion based on unlabeled measurements of markers attached to the body. For that purpose, we employ a given kinematic model of the human body including the locations of the attached markers. Based on the model, we apply a combination of constrained sample-based Kalman filtering and multi-target tracking techniques: 1)...
In this paper, we propose an algorithm for tracking mobile devices (such as smartphones, tablets, or smartglasses) in a known environment for augmented reality applications. For this purpose, we interpret the environment as an extended object with a known shape, and design likelihoods for different types of image features, using association models from extended object tracking. Based on these likelihoods,...
In this paper, we propose a novel approach to track extended objects by incorporating negative information. While traditional techniques to track extended targets use only positive measurements, assumed to stem from the target, the proposed estimator is also capable of incorporating negative measurements, which tell us where the target cannot be. To achieve this, we introduce a simple, robust, and...
We look at the task of estimating the parameters of a geometric constraint from noisy points in 2D. The classical approach of minimizing the Euclidean distance error between points and constraint generally yields biased estimates for nonlinear constraints and higher noise levels. To deal with this issue, the expected distribution of the distance error can be explicitly incorporated in the estimator...
In this paper, we propose a novel approach to track elongated, curved extended targets by representing their shapes with splines. Elongated shapes are forms whose length is much larger than their width, and can be found in many places, such as in connected vehicles like trains, in group targets like a caravan moving along a curved street, or even when estimating the pose of a person. A particular...
We consider the task of recursively estimating the pose and shape parameters of 3D objects based on noisy point cloud measurements from their surface. We focus on objects whose surface can be constructed by transforming a plane curve, such as a cylinder that is constructed by extruding a circle. However, designing estimators for such objects is challenging, as the straightforward distance-minimizing...
An extended object gives rise to several measurements that originate from unknown measurement sources on the object. In this paper, we consider the tracking and parameter estimation of extended objects that are modeled as a curve in 2D such as a circle or an ellipse. A standard model for such extended objects is to assume that the unknown measurement sources are uniformly distributed on the curve...
When tracking an extended object, traditional approaches exploit information only from measurements that are assumed to stem from the target, and discard observations assumed to have been generated elsewhere. However, the fact that these observations were received contains valuable information about where the target is not. This information, which is usually treated as clutter with little value, can...
One of the challenges in shape tracking is how to deal with associating measurements to sources in the shape, while also taking to account parameters such as shape curvature and noise characteristics. Partial Information Models (PIMs) introduce a new approach that addresses this issue. The idea is to reparametrize each measurement into two components, one which depends on the position of its source...
In this paper, we propose a novel algorithm for automatically calibrating a network of depth sensors, based on a moving calibration object. The sensors may have non-overlapping fields of view in order to avoid interference. Two major challenges are discussed. First, depending on where the object is located relative to the sensor, the number and quality of the measurements strongly varies. Second,...
Modeling 2D extended targets with star-convex Random Hypersurface Models (RHMs) allows for accurate object pose and shape estimation. A star-convex RHM models the shape of an object with the aid of a radial function that describes the distance from the object center to any point on its boundary. However, up to now only linear estimators, i.e., Kalman Filters, are used due to the lack of a explicit...
A popular approach when tracking extended objects with elongated shapes, such as ships or airplanes, is to approximate them as a line segment. Despite its simple shape, the distribution of measurement sources on a line segment can be characterized in many radically different ways. The spectrum ranges from Spatial Distribution Models that assume a distinct probability for each individual source, to...
As sensor resolution increases, the accuracy and robustness of tracking algorithms can be improved by incorporating more information about the shape of the target object. This raises the need for simple and robust shape models capable of describing detailed objects. In this paper we propose an approach based on Random Hypersurface Models that interprets target shapes as scaled extrusions. This is...
When tracking extended objects, it is often the case that the shape of the target cannot be fully observed due to issues of visibility, artifacts, or high noise, which can change with time. In these situations, it is a common approach to model targets as simpler shapes instead, such as ellipsoids or cylinders. However, these simplifications cause information loss from the original shape, which could...
This work considers the problem of estimating the parameters of an extended object based on noisy point observations from its boundary. The intention is to explore relationships between common approaches by breaking them down into their basic assumptions within the Bayesian framework. In doing so, we find that distance-minimizing curve fitting algorithms can be modeled by using a special Spatial Distribution...
As sensor resolution increases, estimators tracking extended objects benefit from being able to closely model the shape of the target. However, as more shape details are incorporated, this usually leads to increasingly complex estimators. A more useful approach is to describe these shapes as a combination of simpler shapes connected to each other. In this paper, we propose a modular approach to estimate...
In this paper, we present a novel approach for tracking objects whose movement is constrained to a compact one-dimensional manifold, for example a conveyer belt or a mobile robot whose movement is restricted to tracks. Standard approaches either ignore the constraint at first and retroactively move the estimate to lie on the manifold, or consider the tracking problem on a manifold but falsely assume...
In this paper, we consider the problem of jointly tracking the pose and shape of objects based on noisy data from cameras and depth sensors. Our proposed approach formalizes object silhouettes from image data as measurements within a Bayesian estimation framework. Projecting object silhouettes from images back into space yields a visual hull that constrains the object. In this work, we focus on the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.