Jannik Steinbring

Real-time whole-body human motion tracking based on unlabeled markers

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) joint constraints imposed by the human body are satisfied by introducing a parameter transformation based on periodic functions, 2) a global nearest neighbor (GNN) algorithm computes the most likely one-to-one association between markers and measurements, and 3) multiple hypotheses tracking (MHT) allows for a robust initialization that only requires an upright standing user. Evaluations clearly demonstrate that the proposed tracking provides highly accurate pose estimates in realtime, even for fast and complex motions. In addition, it provides robustness to partial occlusion of markers and also handles unavoidable clutter measurements.

Recursive Bayesian pose and shape estimation of 3D objects using transformed plane curves

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 approach cannot observe all parameters, and additionally is subject to bias in the presence of noise. In this article, we first discuss these issues and then develop probabilistic models for cylinder, torus, cone, and an extruded curve by adapting related approaches including Random Hypersurface Models, partial likelihood, and symmetric shape models. In experiments with simulated data, we show that these models yield unbiased estimators for all parameters even in the presence of high noise.

A closed-form likelihood for Particle Filters to track extended objects with star-convex RHMs

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 likelihood function. In this paper, we propose a closed-form and easy to implement likelihood function for tracking extended targets with star-convex RHMs. This makes it possible to apply nonlinear estimators such as Particle Filters to estimate a detailed shape of a target.We compared the proposed likelihood against the usual Kalman filter approaches with tracking pose and shape of an airplane in 2D. The evaluations showed that the combination of the Progressive Gaussian Filter (PGF) and the new likelihood function delivers the best estimation performance and can outperform the usually employed Kalman Filters.

Exploiting negative measurements for tracking star-convex extended objects

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 easy-to-implement recursive Bayesian estimator which employs ideas from the field of curve fitting. As an application of this idea, we develop a measurement equation to estimate star-convex shapes which can be used in standard non-linear Kalman filters. Finally, we evaluate the proposed estimator using synthetic data and demonstrate its robustness in scenarios with clutter and low measurement quality.

Semi-analytic progressive Gaussian filtering

As an alternative to Kalman filters and particle filters, recently the progressive Gaussian filter (PGF) was proposed for estimating the state of discrete-time stochastic nonlinear dynamic systems. Like Kalman filters, the estimate of the PGF is a Gaussian distribution, but like particle filters, its measurement update works directly with the likelihood function in order to avoid the inherent linearization of the Kalman filters. However, compared to particle filters, the PGF allows for much faster state estimation and circumvents the severe problem of particle degeneracy by gradually transforming its prior Gaussian distribution into a posterior one. In this paper, we further enhance the estimation quality and runtime of the PGF by proposing a semi-analytic measurement update applicable to likelihood functions that only depend on a subspace of the system state. In fact, the proposed semi-analytic measurement update is not limited to the PGF and can be used by any nonlinear state estimator as long as its state estimate is Gaussian, e.g., the Gaussian particle filter.

Directional splitting of Gaussian density in non-linear random variable transformation

Transformation of a random variable is a common need in a design of many algorithms in signal processing, automatic control, and fault detection. Typically, the design is tied to an assumption on a probability density function of the random variable, often in the form of the Gaussian distribution. The assumption may be, however, difficult to be met in algorithms involving non-linear transformation of the random variable. This paper focuses on techniques capable to ensure validity of the Gaussian assumption of the non-linearly transformed Gaussian variable by approximating the to-be-transformed random variable distribution by a Gaussian mixture (GM) distribution. The stress is laid on an analysis and selection of design parameters of the approximate GM distribution to minimise the error imposed by the non-linear transformation such as the location and number of the GM terms. A special attention is devoted to the definition of the novel GM splitting directions based on the measures of non-Gaussianity. The proposed splitting directions are analysed and illustrated in numerical simulations.

Camera- and IMU-based pose tracking for augmented reality

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, and together with sensor information of the inertial measurement unit of the mobile device, we design a recursive Bayesian tracking algorithm. We present results of our first prototype and discuss the lessons we learned from its implementation. In particular, we set up a “pick-by-vision” scenario, where the location of objects in a shelf is to be highlighted in a camera image. Our experiments confirm that the proposed tracking approach achieves accurate and robust tracking results even in scenarios with fast motion.