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.
There are many works that focus on gesture recognition @cite_40 . We instead focus on the tracking, noting that gesture is then easily achieved by comparison of the relevant joint orientations from a reference pose or animation. Instead of simply providing collision geometry to a physical environment @cite_41 @cite_2 , we provide knowledge of specific hand and finger pose, with application to gesture recognition.
@cite_41 @cite_40 @cite_2
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Gesture recognition aims to recognize meaningful movements of human bodies, and is of utmost importance in intelligent human–computer robot interactions. In this paper, we present a multimodal gesture recognition method based on 3-D convolution and convolutional long-short-term-memory (LSTM) networks. The proposed method first learns short-term spatiotemporal features of gestures through the 3-D convolutional neural network, and then learns long-term spatiotemporal features by convolutional LSTM networks based on the extracted short-term spatiotemporal features. In addition, fine-tuning among multimodal data is evaluated, and we find that it can be considered as an optional skill to prevent overfitting when no pre-trained models exist. The proposed method is verified on the ChaLearn LAP large-scale isolated gesture data set (IsoGD) and the Sheffield Kinect gesture (SKIG) data set. The results show that our proposed method can obtain the state-of-the-art recognition accuracy (51.02 on the validation set of IsoGD and 98.89 on SKIG). Gesture recognition has been a research focus with the popularity of depth sensing device. In this paper, we propose a new fingertip detection method based on a novel definition of fingers. This method consists of two steps. Firstly, finger bases are detected and estimated as prior information. Secondly, the finger regions and fingertips are located. In the second module, the point cloud of hand is represented as a graph to obtain all geodesic paths originated from palm center. If one path travels through a finger base, then its terminal point is defined as a finger point. The fingertips are determined within these finger points by utilizing geodesic distances. To our knowledge, such definition has never been applied in gesture recognition before and its performance surpasses the definition of geodesic maxima. Experimental results demonstrates the effectiveness of our method even when hand is not parallel to camera. Compared with state-of-the-art approach, our method shows much less error. We present our approach for visual tracking of head, hands and head orientation. Given the images provided by a calibrated stereo-camera, color and disparity information are integrated into a multi-hypotheses tracking framework in order to find the 3D-positions of the respective body parts. Based on the hands' motion, an HMM-based approach is applied to recognize pointing gestures. We show experimentally, that the gesture recognition performance can be improved significantly by using visually gained information about head orientation as an additional feature. Our system aims at applications in the field of human-robot interaction, where it is important to do run-on recognition in real-time, to allow for robot's egomotion and not to rely on manual initialization. In this paper we propose a hand gesture recognition system that relies on color and depth images, and on a small pose sensor on the human palm. Monocular and stereo vision systems have been used for human pose and gesture recognition, but with limited scope due to limitations on texture, illumination, etc. New RGB-Depth sensors, that reply on projected light such as the Microsoft Kinect, have overcome many of those limitations. However, the point clouds for hand gestures are still in many cases noisy and partially occluded, and hand gesture recognition is not trivial. Hand gesture recognition is much more complex than full body motion, since we can have the hands in any orientation and can not assume a standing body on a ground plane. In this work we propose to add a tiny pose sensor to the human palm, with a minute accelerometer and magnetometer that combined provide 3D angular pose, to reduce the search space and have a robust and computationally light recognition method. Starting with the full depth image point cloud, segmentation can be performed by taking into account the relative depth and hand orientation, as well as skin color. Identification is then performed by matching 3D voxel occupancy against a gesture template database. Preliminary results are presented for the recognition of Portuguese Sign Language alphabet, showing the validity of the approach.