Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model

Real-time marker-less hand tracking is of increasing importance in human-computer interaction. Robust and accurate tracking of arbitrary hand motion is a challenging problem due to the many degrees of freedom, frequent self-occlusions, fast motions, and uniform skin color. In this paper, we propose a new approach that tracks the full skeleton motion of the hand from multiple RGB cameras in real-time. The main contributions include a new generative tracking method which employs an implicit hand shape representation based on Sum of Anisotropic Gaussians (SAG), and a pose fitting energy that is smooth and analytically differentiable making fast gradient based pose optimization possible. This shape representation, together with a full perspective projection model, enables more accurate hand modeling than a related baseline method from literature. Our method achieves better accuracy than previous methods and runs at 25 fps. We show these improvements both qualitatively and quantitatively on publicly available datasets.Comment: 8 pages, Accepted version of paper published at 3DV 201

Automated tracking of colloidal clusters with sub-pixel accuracy and precision

Quantitative tracking of features from video images is a basic technique employed in many areas of science. Here, we present a method for the tracking of features that partially overlap, in order to be able to track so-called colloidal molecules. Our approach implements two improvements into existing particle tracking algorithms. Firstly, we use the history of previously identified feature locations to successfully find their positions in consecutive frames. Secondly, we present a framework for non-linear least-squares fitting to summed radial model functions and analyze the accuracy (bias) and precision (random error) of the method on artificial data. We find that our tracking algorithm correctly identifies overlapping features with an accuracy below 0.2% of the feature radius and a precision of 0.1 to 0.01 pixels for a typical image of a colloidal cluster. Finally, we use our method to extract the three-dimensional diffusion tensor from the Brownian motion of colloidal dimers.Comment: 20 pages, 8 figures. Non-revised preprint version, please refer to http://dx.doi.org/10.1088/1361-648X/29/4/04400

GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions. The algorithm can be seen as a combination of a sampling-based filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by sampling the state distribution and propagating each sample through the dynamic system and observation models. On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM outperforms several GP-Bayes and Particle Filters on a standard benchmark. We also demonstrate its use in a pushing task, predicting with experimental accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure

Capturing Hands in Action using Discriminative Salient Points and Physics Simulation

Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.Comment: Accepted for publication by the International Journal of Computer Vision (IJCV) on 16.02.2016 (submitted on 17.10.14). A combination into a single framework of an ECCV'12 multicamera-RGB and a monocular-RGBD GCPR'14 hand tracking paper with several extensions, additional experiments and detail

Dual Quaternions as Constraints in 4D-DPM Models for Pose Estimation

This work was partially financed by Plan Nacional de Investigacion y Desarrollo (I+D), Comision Interministerial de Ciencia y Tecnologia (FEDER-CICYT) under the project DPI2013-44227-R.Martínez Bertí, E.; Sánchez Salmerón, AJ.; Ricolfe Viala, C. (2017). Dual Quaternions as Constraints in 4D-DPM Models for Pose Estimation. Sensors. 17 (8)(1913):1-16. https://doi.org/10.3390/s17081913S11617 (8)191

Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

Tracking hands in action for gesture-based computer input

This thesis introduces new methods for markerless tracking of the full articulated motion of hands and for informing the design of gesture-based computer input. Emerging devices such as smartwatches or virtual/augmented reality glasses are in need of new input devices for interaction on the move. The highly dexterous human hands could provide an always-on input capability without the actual need to carry a physical device. First, we present novel methods to address the hard computer vision-based hand tracking problem under varying number of cameras, viewpoints, and run-time requirements. Second, we contribute to the design of gesture-based interaction techniques by presenting heuristic and computational approaches. The contributions of this thesis allow users to effectively interact with computers through markerless tracking of hands and objects in desktop, mobile, and egocentric scenarios.Diese Arbeit stellt neue Methoden für die markerlose Verfolgung der vollen Artikulation der Hände und für die Informierung der Gestaltung der Gestik-Computer-Input. Emerging-Geräte wie Smartwatches oder virtuelle / Augmented-Reality-Brillen benötigen neue Eingabegeräte für Interaktion in Bewegung. Die sehr geschickten menschlichen Hände konnten eine immer-on-Input-Fähigkeit, ohne die tatsächliche Notwendigkeit, ein physisches Gerät zu tragen. Zunächst stellen wir neue Verfahren vor, um das visionbasierte Hand-Tracking-Problem des Hardcomputers unter variierender Anzahl von Kameras, Sichtweisen und Laufzeitanforderungen zu lösen. Zweitens tragen wir zur Gestaltung von gesture-basierten Interaktionstechniken bei, indem wir heuristische und rechnerische Ansätze vorstellen. Die Beiträge dieser Arbeit ermöglichen es Benutzern, effektiv interagieren mit Computern durch markerlose Verfolgung von Händen und Objekten in Desktop-, mobilen und egozentrischen Szenarien

Tracking hands in action for gesture-based computer input

This thesis introduces new methods for markerless tracking of the full articulated motion of hands and for informing the design of gesture-based computer input. Emerging devices such as smartwatches or virtual/augmented reality glasses are in need of new input devices for interaction on the move. The highly dexterous human hands could provide an always-on input capability without the actual need to carry a physical device. First, we present novel methods to address the hard computer vision-based hand tracking problem under varying number of cameras, viewpoints, and run-time requirements. Second, we contribute to the design of gesture-based interaction techniques by presenting heuristic and computational approaches. The contributions of this thesis allow users to effectively interact with computers through markerless tracking of hands and objects in desktop, mobile, and egocentric scenarios.Diese Arbeit stellt neue Methoden für die markerlose Verfolgung der vollen Artikulation der Hände und für die Informierung der Gestaltung der Gestik-Computer-Input. Emerging-Geräte wie Smartwatches oder virtuelle / Augmented-Reality-Brillen benötigen neue Eingabegeräte für Interaktion in Bewegung. Die sehr geschickten menschlichen Hände konnten eine immer-on-Input-Fähigkeit, ohne die tatsächliche Notwendigkeit, ein physisches Gerät zu tragen. Zunächst stellen wir neue Verfahren vor, um das visionbasierte Hand-Tracking-Problem des Hardcomputers unter variierender Anzahl von Kameras, Sichtweisen und Laufzeitanforderungen zu lösen. Zweitens tragen wir zur Gestaltung von gesture-basierten Interaktionstechniken bei, indem wir heuristische und rechnerische Ansätze vorstellen. Die Beiträge dieser Arbeit ermöglichen es Benutzern, effektiv interagieren mit Computern durch markerlose Verfolgung von Händen und Objekten in Desktop-, mobilen und egozentrischen Szenarien