Tracking the full skelet al pose of the hands and fingers is a challenging problem that has a plethora of applications for user interaction. Existing techniques either require wearable hardware, add restrictions to user pose, or require significant computation resources. This research explores a new approach to tracking hands, or any articulated model, by using an augmented rigid body simulation. This allows us to phrase 3D object tracking as a linear complementarity problem with a well-defined solution. Based on a depth sensor's samples, the system generates constraints that limit motion orthogonal to the rigid body model's surface. These constraints, along with prior motion, collision contact constraints, and joint mechanics, are resolved with a projected Gauss-Seidel solver. Due to camera noise properties and attachment errors, the numerous surface constraints are impulse capped to avoid overpowering mechanical constraints. To improve tracking accuracy, multiple simulations are spawned at each frame and fed a variety of heuristics, constraints and poses. A 3D error metric selects the best-fit simulation, helping the system handle challenging hand motions. Such an approach enables real-time, robust, and accurate 3D skelet al tracking of a user's hand on a variety of depth cameras, while only utilizing a single x86 CPU core for processing.
Hand tracking began with the VPL dataglove @cite_14 . More recently, a classifier was trained to recognize the pose of a specifically colored glove as seen by a camera - a much less expensive setup @cite_38 . Microsoft Digits avoids using special markers on the fingers themselves, instead placing a camera on the wrist @cite_0 . To maximize consumer convenience, the eventual goal is to be able to track the hand without any apparatus @cite_17 .
@cite_0 @cite_38 @cite_14 @cite_17
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This paper presents a novel and real-time system for interaction with an application or video game via hand gestures. Our system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application. In the training stage, after extracting the keypoints for every training image using the scale invariance feature transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multiclass SVM to build the training classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using our algorithm, then, the keypoints are extracted for every small image that contains the detected hand gesture only and fed into the cluster model to map them into a bag-of-words vector, which is finally fed into the multiclass SVM training classifier to recognize the hand gesture. Reliable hand detection and tracking in passive 2D video still remains a challenge. Yet the consumer market for gesture-based interaction is expanding rapidly and surveillance systems that can deduce fine-grained human activities involving hand and arm postures are in high demand. In this paper, we present a hand tracking method that does not require reliable detection. We built it on top of “Flocks of Features” which combines grey-level optical flow, a “flocking” constraint, and a learned foreground color distribution. By adding probabilistic (instead of binary classified) detections based on grey-level appearance as an additional image cue, we show improved tracking performance despite rapid hand movements and posture changes. This helps overcome tracking difficulties in texture-rich and skin-colored environments, improving performance on a 10-minute collection of video clips from 75 to 86 (see examples on our website). Modern systems for real-time hand tracking rely on a combination of discriminative and generative approaches to robustly recover hand poses. Generative approaches require the specification of a geometric model. In this paper, we propose a the use of sphere-meshes as a novel geometric representation for real-time generative hand tracking. How tightly this model fits a specific user heavily affects tracking precision. We derive an optimization to non-rigidly deform a template model to fit the user data in a number of poses. This optimization jointly captures the user's static and dynamic hand geometry, thus facilitating high-precision registration. At the same time, the limited number of primitives in the tracking template allows us to retain excellent computational performance. We confirm this by embedding our models in an open source real-time registration algorithm to obtain a tracker steadily running at 60Hz. We demonstrate the effectiveness of our solution by qualitatively and quantitatively evaluating tracking precision on a variety of complex motions. We show that the improved tracking accuracy at high frame-rate enables stable tracking of extended and complex motion sequences without the need for per-frame re-initialization. To enable further research in the area of high-precision hand tracking, we publicly release source code and evaluation datasets. Abstract This paper describes the development of a remote handling control of an anthropomorphic robotic hand, the SCHUNK S5FH, using the human hand as master by measuring its motion with OptiTrack Technology. The goal of this work is to enhance manipulation studies on the human hand and to instantly transfer those studies on robotic hands. A preliminary study on methods and devices used for fingers tracking led to the choice of a simplified kinematic model of the human hand on the basis of the available motion tracking system. Using the same criteria, the analysis of protocols for markers allocation led to define the number and a method for their arrangement on the fingers and palm. In order to overcome the limitation of the Motion Capture System, a method for identification and labeling has been developed according to their anatomical arrangement. Afterwards, the tracking is performed using the constraints between marker positions on the kinematic chain of the hand and a dynamic labeling algorithm robust with respect to noise, outliers and loss of markers. The validation is performed using the right hand of different subjects and considering different tasks involving flexion extension and abduction adduction of fingers and thumb opposition. For testing and validation, preliminary studies on synergies for manipulation tasks such as screwing a cup, has been conducted on the human hand and transferred on the robotic hand.