Hand motion capture has been an active research topic, following the success of full-body pose tracking. Despite similarities, hand tracking proves to be more challenging, characterized by a higher dimensionality, severe occlusions and self-similarity between fingers. For this reason, most approaches rely on strong assumptions, like hands in isolation or expensive multi-camera systems, that limit practical use. In this work, we propose a framework for hand tracking that can capture the motion of two interacting hands using only a single, inexpensive RGB-D camera. Our approach combines a generative model with collision detection and discriminatively learned salient points. We quantitatively evaluate our approach on 14 new sequences with challenging interactions.
The demand for flexible and re-programmable robots has increased the need for programming by demonstration systems. In this paper, grasp recognition is considered in a programming by demonstration framework. Three methods for grasp recognition are presented and evaluated. The first method uses Hidden Markov Models to model the hand posture sequence during the grasp sequence, while the second method relies on the hand trajectory and hand rotation. The third method is a hybrid method, in which both the first two methods are active in parallel. The particular contribution is that all methods rely on the grasp sequence and not just the final posture of the hand. This facilitates grasp recognition before the grasp is completed. Also, by analyzing the entire sequence and not just the final grasp, the decision is based on more information and increased robustness of the overall system is achieved. The experimental results show that both arm trajectory and final hand posture provide important information for grasp classification. By combining them, the recognition rate of the overall system is increased. In this paper we first describe how we have constructed a 3D deformable Point Distribution Model of the human hand, capturing training data semi-automatically from volume images via a physically-based model. We then show how we have attempted to use this model in tracking an unmarked hand moving with 6 degrees of freedom (plus deformation) in real time using a single video camera. In the course of this we show how to improve on a weighted least-squares pose parameter approximation at little computational cost. We note the successes and shortcomings of our system and discuss how it might be improved. A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclidean space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this clutter tolerant approach is demonstrated in quantitative experiments with hundreds of real hand images. Passive sensing of human hand and limb motion is important for a wide range of applications from human-computer interaction to athletic performance measurement. High degree of freedom articulated mechanisms like the human hand are difficult to track because of their large state space and complex image appearance. This article describes a model-based hand tracking system, called DigitEyes, that can recover the state of a 27 DOF hand model from ordinary gray scale images at speeds of up to 10 Hz. This paper compares two methods for object localization from contours: shape context and chamfer matching of templates. In the light of our experiments, we suggest improvements to the shape context: shape contexts are used to find corresponding features between model and image. In real images it is shown that the shape context is highly influenced by clutters; furthermore, even when the object is correctly localized, the feature correspondence may be poor. We show that the robustness of shape matching can be increased by including a figural continuity constraint. The combined shape and continuity cost is minimized using the Viterbi algorithm on features, resulting in improved localization and correspondence. Our algorithm can be generally applied to any feature based shape matching method. Chamfer matching correlates model templates with the distance transform of the edge image. This can be done efficiently using a coarse-to-fine search over the transformation parameters. The method is robust in clutter, however, multiple templates are needed to handle scale, rotation and shape variation. We compare both methods for locating hand shapes in cluttered images, and applied to word recognition in EZ-Gimpy images. Capturing the motion of two hands interacting with an object is a very challenging task due to the large number of degrees of freedom, self-occlusions, and similarity between the fingers, even in the case of multiple cameras observing the scene. In this paper we propose to use discriminatively learned salient points on the fingers and to estimate the finger-salient point associations simultaneously with the estimation of the hand pose. We introduce a differentiable objective function that also takes edges, optical flow and collisions into account. Our qualitative and quantitative evaluations show that the proposed approach achieves very accurate results for several challenging sequences containing hands and objects in action. We propose a method that relies on markerless visual observations to track the full articulation of two hands that interact with each-other in a complex, unconstrained manner. We formulate this as an optimization problem whose 54-dimensional parameter space represents all possible configurations of two hands, each represented as a kinematic structure with 26 Degrees of Freedom (DoFs). To solve this problem, we employ Particle Swarm Optimization (PSO), an evolutionary, stochastic optimization method with the objective of finding the two-hands configuration that best explains observations provided by an RGB-D sensor. To the best of our knowledge, the proposed method is the first to attempt and achieve the articulated motion tracking of two strongly interacting hands. Extensive quantitative and qualitative experiments with simulated and real world image sequences demonstrate that an accurate and efficient solution of this problem is indeed feasible. In several hand-object(s) interaction scenarios, the change in the objects' state is a direct consequence of the hand's motion. This has a straightforward representation in Newtonian dynamics. We present the first approach that exploits this observation to perform model-based 3D tracking of a table-top scene comprising passive objects and an active hand. Our forward modelling of 3D hand-object(s) interaction regards both the appearance and the physical state of the scene and is parameterized over the hand motion (26 DoFs) between two successive instants in time. We demonstrate that our approach manages to track the 3D pose of all objects and the 3D pose and articulation of the hand by only searching for the parameters of the hand motion. In the proposed framework, covert scene state is inferred by connecting it to the overt state, through the incorporation of physics. Thus, our tracking approach treats a variety of challenging observability issues in a principled manner, without the need to resort to heuristics. Computer sensing of hand and limb motion is an important problem for applications in human computer interaction and computer graphics. We describe a framework for local trading of self occluding motion, in which one part of an object obstructs the visibility of another. Our approach uses a kinematic model to predict occlusions and windowed templates to track partially occluded objects. We present offline 3D tracking results for hand motion with significant self occlusion. > Digits is a wrist-worn sensor that recovers the full 3D pose of the user's hand. This enables a variety of freehand interactions on the move. The system targets mobile settings, and is specifically designed to be low-power and easily reproducible using only off-the-shelf hardware. The electronics are self-contained on the user's wrist, but optically image the entirety of the user's hand. This data is processed using a new pipeline that robustly samples key parts of the hand, such as the tips and lower regions of each finger. These sparse samples are fed into new kinematic models that leverage the biomechanical constraints of the hand to recover the 3D pose of the user's hand. The proposed system works without the need for full instrumentation of the hand (for example using data gloves), additional sensors in the environment, or depth cameras which are currently prohibitive for mobile scenarios due to power and form-factor considerations. We demonstrate the utility of Digits for a variety of application scenarios, including 3D spatial interaction with mobile devices, eyes-free interaction on-the-move, and gaming. We conclude with a quantitative and qualitative evaluation of our system, and discussion of strengths, limitations and future work.  We present a novel solution to the problem of recovering and tracking the 3D position, orientation and full articulation of a human hand from markerless visual observations obtained by a Kinect sensor. We treat this as an optimization problem, seeking for the hand model parameters that minimize the discrepancy between the appearance and 3D structure of hypothesized instances of a hand model and actual hand observations. This optimization problem is effectively solved using a variant of Particle Swarm Optimization (PSO). The proposed method does not require special markers and or a complex image acquisition setup. Being model based, it provides continuous solutions to the problem of tracking hand articulations. Extensive experiments with a prototype GPU-based implementation of the proposed method demonstrate that accurate and robust 3D tracking of hand articulations can be achieved in near real-time (15Hz). We present a method for tracking a hand while it is interacting with an object. This setting is arguably the one where hand-tracking has most practical relevance, but poses significant additional challenges: strong occlusions by the object as well as self-occlusions are the norm, and classical anatomical constraints need to be softened due to the external forces between hand and object. To achieve robustness to partial occlusions, we use an individual local tracker for each segment of the articulated structure. The segments are connected in a pairwise Markov random field, which enforces the anatomical hand structure through soft constraints on the joints between adjacent segments. The most likely hand configuration is found with belief propagation. Both range and color data are used as input. Experiments are presented for synthetic data with ground truth and for real data of people manipulating objects. Articulated hand-tracking systems have been widely used in virtual reality but are rarely deployed in consumer applications due to their price and complexity. In this paper, we propose an easy-to-use and inexpensive system that facilitates 3-D articulated user-input using the hands. Our approach uses a single camera to track a hand wearing an ordinary cloth glove that is imprinted with a custom pattern. The pattern is designed to simplify the pose estimation problem, allowing us to employ a nearest-neighbor approach to track hands at interactive rates. We describe several proof-of-concept applications enabled by our system that we hope will provide a foundation for new interactions in modeling, animation control and augmented reality. Partitioned sampling is a technique which was introduced in [I7] for avoiding the high cost of particle filters when tracking more than one object. In fact this technique can reduce the curse of dimensionality in other situations too. This paper describes how to use partitioned sampling on articulated objects, obtaining results that would be impossible with standard sampling methods. Because partitioned sampling is the statistical analogue of a hierarchical search, it makes sense to use it on articulated objects, since links at the base of the object can be localised before moving on to search for subsequent links. This paper presents a practical technique for model-based 3D hand tracking. An anatomically accurate hand model is built from truncated quadrics. This allows for the generation of 2D profiles of the model using elegant tools from projective geometry, and for an efficient method to handle self-occlusion. The pose of the hand model is estimated with an Unscented Kalman filter (UKF), which minimizes the geometric error between the profiles and edges extracted from the images. The use of the UKF permits higher frame rates than more sophisticated estimation methods such as particle filtering, whilst providing higher accuracy than the extended Kalman filter The system is easily scalable from single to multiple views, and from rigid to articulated models. First experiments on real data using one and two cameras demonstrate the quality of the proposed method for tracking a 7 DOF hand model. Tracking the articulated 3D motion of the hand has important applications, for example, in human-computer interaction and teleoperation. We present a novel method that can capture a broad range of articulated hand motions at interactive rates. Our hybrid approach combines, in a voting scheme, a discriminative, part-based pose retrieval method with a generative pose estimation method based on local optimization. Color information from a multi-view RGB camera setup along with a person-specific hand model are used by the generative method to find the pose that best explains the observed images. In parallel, our discriminative pose estimation method uses fingertips detected on depth data to estimate a complete or partial pose of the hand by adopting a part-based pose retrieval strategy. This part-based strategy helps reduce the search space drastically in comparison to a global pose retrieval strategy. Quantitative results show that our method achieves state-of-the-art accuracy on challenging sequences and a near-real time performance of 10 fps on a desktop computer. Due to occlusions, the estimation of the full pose of a human hand interacting with an object is much more challenging than pose recovery of a hand observed in isolation. In this work we formulate an optimization problem whose solution is the 26-DOF hand pose together with the pose and model parameters of the manipulated object. Optimization seeks for the joint hand-object model that (a) best explains the incompleteness of observations resulting from occlusions due to hand-object interaction and (b) is physically plausible in the sense that the hand does not share the same physical space with the object. The proposed method is the first that solves efficiently the continuous, full-DOF, joint hand-object tracking problem based solely on markerless multicamera input. Additionally, it is the first to demonstrate how hand-object interaction can be exploited as a context that facilitates hand pose estimation, instead of being considered as a complicating factor. Extensive quantitative and qualitative experiments with simulated and real world image sequences as well as a comparative evaluation with a state-of-the-art method for pose estimation of isolated hands, support the above findings. In this paper, we propose a prior for hand pose estimation that integrates the direct relation between a manipulating hand and a 3d object. This is of particular interest for a variety of applications since many tasks performed by humans require hand-object interaction. Inspired by the ability of humans to learn the handling of an object from a single example, our focus lies on very sparse training data. We express estimated hand poses in local object coordinates and extract for each individual hand segment, the relative position and orientation as well as contact points on the object. The prior is then modeled as a spatial distribution conditioned to the object. Given a new object of the same object class and new hand dimensions, we can transfer the prior by a procedure involving a geometric warp. In our experiments, we demonstrate that the prior may be used to improve the robustness of a 3d hand tracker and to synthesize a new hand grasping a new object. For this, we integrate the prior into a unified belief propagation framework for tracking and synthesis.