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

Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery

Automatic target tracking in airborne FLIR imagery is currently a challenge due to the camera ego-motion. This phenomenon distorts the spatio-temporal correlation of the video sequence, which dramatically reduces the tracking performance. Several works address this problem using ego-motion compensation strategies. They use a deterministic approach to compensate the camera motion assuming a specific model of geometric transformation. However, in real sequences a specific geometric transformation can not accurately describe the camera ego-motion for the whole sequence, and as consequence of this, the performance of the tracking stage can significantly decrease, even completely fail. The optimum transformation for each pair of consecutive frames depends on the relative depth of the elements that compose the scene, and their degree of texturization. In this work, a novel Particle Filter framework is proposed to efficiently manage several hypothesis of geometric transformations: Euclidean, affine, and projective. Each type of transformation is used to compute candidate locations of the object in the current frame. Then, each candidate is evaluated by the measurement model of the Particle Filter using the appearance information. This approach is able to adapt to different camera ego-motion conditions, and thus to satisfactorily perform the tracking. The proposed strategy has been tested on the AMCOM FLIR dataset, showing a high efficiency in the tracking of different types of targets in real working conditions

3D Localization and Tracking Methods for Multi-Platform Radar Networks

Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algorithms for efficient fusion of information obtained through multiple receivers has attracted much attention. However, considerable challenges remain. This article provides an overview on recent unconstrained and constrained localization techniques as well as multitarget tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing methods are illustrated and explored in detail, one aimed at accomplishing localization tasks the other tracking functions. As to the former, assuming a MPRN with one transmitter and multiple receivers, the angular and range constrained estimator (ARCE) algorithm capitalizes on the knowledge of the transmitter antenna beamwidth. As to the latter, the scalable sum-product algorithm (SPA) based MTT technique is presented. Additionally, a solution to combine ARCE and SPA-based MTT is investigated in order to boost the accuracy of the overall surveillance system. Simulated experiments show the benefit of the combined algorithm in comparison with the conventional baseline SPA-based MTT and the stand-alone ARCE localization, in a 3D sensing scenario

Learning Articulated Motions From Visual Demonstration

Many functional elements of human homes and workplaces consist of rigid components which are connected through one or more sliding or rotating linkages. Examples include doors and drawers of cabinets and appliances; laptops; and swivel office chairs. A robotic mobile manipulator would benefit from the ability to acquire kinematic models of such objects from observation. This paper describes a method by which a robot can acquire an object model by capturing depth imagery of the object as a human moves it through its range of motion. We envision that in future, a machine newly introduced to an environment could be shown by its human user the articulated objects particular to that environment, inferring from these "visual demonstrations" enough information to actuate each object independently of the user. Our method employs sparse (markerless) feature tracking, motion segmentation, component pose estimation, and articulation learning; it does not require prior object models. Using the method, a robot can observe an object being exercised, infer a kinematic model incorporating rigid, prismatic and revolute joints, then use the model to predict the object's motion from a novel vantage point. We evaluate the method's performance, and compare it to that of a previously published technique, for a variety of household objects.Comment: Published in Robotics: Science and Systems X, Berkeley, CA. ISBN: 978-0-9923747-0-

Tracking and Fusion Methods for Extended Targets Parameterized by Center, Orientation, and Semi-axes

The improvements in sensor technology, e.g., the development of automotive Radio Detection and Ranging (RADAR) or Light Detection and Ranging (LIDAR), which are able to provide a higher detail of the sensor’s environment, have introduced new opportunities but also new challenges to target tracking. In classic target tracking, targets are assumed as points. However, this assumption is no longer valid if targets occupy more than one sensor resolution cell, creating the need for extended targets, modeling the shape in addition to the kinematic parameters. Different shape models are possible and this thesis focuses on an elliptical shape, parameterized with center, orientation, and semi-axes lengths. This parameterization can be used to model rectangles as well. Furthermore, this thesis is concerned with multi-sensor fusion for extended targets, which can be used to improve the target tracking by providing information gathered from different sensors or perspectives. We also consider estimation of extended targets, i.e., to account for uncertainties, the target is modeled by a probability density, so we need to find a so-called point estimate. Extended target tracking provides a variety of challenges due to the spatial extent, which need to be handled, even for basic shapes like ellipses and rectangles. Among these challenges are the choice of the target model, e.g., how the measurements are distributed across the shape. Additional challenges arise for sensor fusion, as it is unclear how to best consider the geometric properties when combining two extended targets. Finally, the extent needs to be involved in the estimation. Traditional methods often use simple uniform distributions across the shape, which do not properly portray reality, while more complex methods require the use of optimization techniques or large amounts of data. In addition, for traditional estimation, metrics such as the Euclidean distance between state vectors are used. However, they might no longer be valid because they do not consider the geometric properties of the targets’ shapes, e.g., rotating an ellipse by 180 degree results in the same ellipse, but the Euclidean distance between them is not 0. In multi-sensor fusion, the same holds, i.e., simply combining the corresponding elements of the state vectors can lead to counter-intuitive fusion results. In this work, we compare different elliptic trackers and discuss more complex measurement distributions across the shape’s surface or contour. Furthermore, we discuss the problems which can occur when fusing extended target estimates from different sensors and how to handle them by providing a transformation into a special density. We then proceed to discuss how a different metric, namely the Gaussian Wasserstein (GW) distance, can be used to improve target estimation. We define an estimator and propose an approximation based on an extension of the square root distance. It can be applied on the posterior densities of the aforementioned trackers to incorporate the unique properties of ellipses in the estimation process. We also discuss how this can be applied to rectangular targets as well. Finally, we evaluate and discuss our approaches. We show the benefits of more complex target models in simulations and on real data and we demonstrate our estimation and fusion approaches compared to classic methods on simulated data.2022-01-2