Heterogeneous hand gesture recognition using 3D dynamic skeletal data

International audienceHand gestures are the most natural and intuitive non-verbal communication medium while interacting with a computer, and related research efforts have recently boosted interest. Additionally, the identifiable features of the hand pose provided by current commercial inexpensive depth cameras can be exploited in various gesture recognition based systems, especially for Human-Computer Interaction. In this paper, we focus our attention on 3D dynamic gesture recognition systems using the hand pose information. Specifically, we use the natural structure of the hand topology-called later hand skeletal data-to extract effective hand kinematic descriptors from the gesture sequence. Descriptors are then encoded in a statistical and temporal representation using respectively a Fisher kernel and a multi-level temporal pyramid. A linear SVM classifier can be applied directly on the feature vector computed over the whole presegmented gesture to perform the recognition. Furthermore, for early recognition from continuous stream, we introduced a prior gesture detection phase achieved using a binary classifier before the final gesture recognition. The proposed approach is evaluated on three hand gesture datasets containing respectively 10, 14 and 25 gestures with specific challenging tasks. Also, we conduct an experiment to assess the influence of depth-based hand pose estimation on our approach. Experimental results demonstrate the potential of the proposed solution in terms of hand gesture recognition and also for a low-latency gesture recognition. Comparative results with state-of-the-art methods are reported

A Transformer-Based Network for Dynamic Hand Gesture Recognition

Transformer-based neural networks represent a successful self-attention mechanism that achieves state-of-the-art results in language understanding and sequence modeling. However, their application to visual data and, in particular, to the dynamic hand gesture recognition task has not yet been deeply investigated. In this paper, we propose a transformer-based architecture for the dynamic hand gesture recognition task. We show that the employment of a single active depth sensor, specifically the usage of depth maps and the surface normals estimated from them, achieves state-of-the-art results, overcoming all the methods available in the literature on two automotive datasets, namely NVidia Dynamic Hand Gesture and Briareo. Moreover, we test the method with other data types available with common RGB-D devices, such as infrared and color data. We also assess the performance in terms of inference time and number of parameters, showing that the proposed framework is suitable for an online in-car infotainment system

Real-time sign language recognition using a consumer depth camera

Gesture recognition remains a very challenging task in the field of computer vision and human computer interaction (HCI). A decade ago the task seemed to be almost unsolvable with the data provided by a single RGB camera. Due to recent advances in sensing technologies, such as time-of-flight and structured light cameras, there are new data sources available, which make hand gesture recognition more feasible. In this work, we propose a highly precise method to recognize static gestures from a depth data, provided from one of the above mentioned devices. The depth images are used to derive rotation-, translation- and scale- invariant features. A multi-layered random forest (MLRF) is then trained to classify the feature vectors, which yields to the recognition of the hand signs. The training time and memory required by MLRF are much smaller, compared to a simple random forest with equivalent precision. This allows to repeat the training procedure of MLRF without significant effort. To show the advantages of our technique, we evaluate our algorithm on synthetic data, on publicly available dataset, containing 24 signs from American Sign Language(ASL) and on a new dataset, collected using recently appeared Intel Creative Gesture Camera. 1

Hand Tracking based on Hierarchical Clustering of Range Data

Fast and robust hand segmentation and tracking is an essential basis for gesture recognition and thus an important component for contact-less human-computer interaction (HCI). Hand gesture recognition based on 2D video data has been intensively investigated. However, in practical scenarios purely intensity based approaches suffer from uncontrollable environmental conditions like cluttered background colors. In this paper we present a real-time hand segmentation and tracking algorithm using Time-of-Flight (ToF) range cameras and intensity data. The intensity and range information is fused into one pixel value, representing its combined intensity-depth homogeneity. The scene is hierarchically clustered using a GPU based parallel merging algorithm, allowing a robust identification of both hands even for inhomogeneous backgrounds. After the detection, both hands are tracked on the CPU. Our tracking algorithm can cope with the situation that one hand is temporarily covered by the other hand.Comment: Technical Repor

Improving gesture recognition through spatial focus of attention

2018 Fall.Includes bibliographical references.Gestures are a common form of human communication and important for human computer interfaces (HCI). Most recent approaches to gesture recognition use deep learning within multi- channel architectures. We show that when spatial attention is focused on the hands, gesture recognition improves significantly, particularly when the channels are fused using a sparse network. We propose an architecture (FOANet) that divides processing among four modalities (RGB, depth, RGB flow, and depth flow), and three spatial focus of attention regions (global, left hand, and right hand). The resulting 12 channels are fused using sparse networks. This architecture improves performance on the ChaLearn IsoGD dataset from a previous best of 67.71% to 82.07%, and on the NVIDIA dynamic hand gesture dataset from 83.8% to 91.28%. We extend FOANet to perform gesture recognition on continuous streams of data. We show that the best temporal fusion strategies for multi-channel networks depends on the modality (RGB vs depth vs flow field) and target (global vs left hand vs right hand) of the channel. The extended architecture achieves optimum performance using Gaussian Pooling for global channels, LSTMs for focused (left hand or right hand) flow field channels, and late Pooling for focused RGB and depth channels. The resulting system achieves a mean Jaccard Index of 0.7740 compared to the previous best result of 0.6103 on the ChaLearn ConGD dataset without first pre-segmenting the videos into single gesture clips. Human vision has α and β channels for processing different modalities in addition to spatial attention similar to FOANet. However, unlike FOANet, attention is not implemented through separate neural channels. Instead, attention is implemented through top-down excitation of neurons corresponding to specific spatial locations within the α and β channels. Motivated by the covert attention in human vision, we propose a new architecture called CANet (Covert Attention Net), that merges spatial attention channels while preserving the concept of attention. The focus layers of CANet allows it to focus attention on hands without having dedicated attention channels. CANet outperforms FOANet by achieving an accuracy of 84.79% on ChaLearn IsoGD dataset while being efficient (≈35% of FOANet parameters and ≈70% of FOANet operations). In addition to producing state-of-the-art results on multiple gesture recognition datasets, this thesis also tries to understand the behavior of multi-channel networks (a la FOANet). Multi- channel architectures are becoming increasingly common, setting the state of the art for performance in gesture recognition and other domains. Unfortunately, we lack a clear explanation of why multi-channel architectures outperform single channel ones. This thesis considers two hypotheses. The Bagging hypothesis says that multi-channel architectures succeed because they average the result of multiple unbiased weak estimators in the form of different channels. The Society of Experts (SoE) hypothesis suggests that multi-channel architectures succeed because the channels differentiate themselves, developing expertise with regard to different aspects of the data. Fusion layers then get to combine complementary information. This thesis presents two sets of experiments to distinguish between these hypotheses and both sets of experiments support the SoE hypothesis, suggesting multi-channel architectures succeed because their channels become specialized. Finally we demonstrate the practical impact of the gesture recognition techniques discussed in this thesis in the context of a sophisticated human computer interaction system. We developed a prototype system with a limited form of peer-to-peer communication in the context of blocks world. The prototype allows the users to communicate with the avatar using gestures and speech and make the avatar build virtual block structures

Real-time hand gesture recognition exploiting multiple 2D and 3D cues

The recent introduction of several 3D applications and stereoscopic display technologies has created the necessity of novel human-machine interfaces. The traditional input devices, such as keyboard and mouse, are not able to fully exploit the potential of these interfaces and do not offer a natural interaction. Hand gestures provide, instead, a more natural and sometimes safer way of interacting with computers and other machines without touching them. The use cases for gesture-based interfaces range from gaming to automatic sign language interpretation, health care, robotics, and vehicle automation. Automatic gesture recognition is a challenging problem that has been attaining a growing interest in the research field for several years due to its applications in natural interfaces. The first approaches, based on the recognition from 2D color pictures or video only, suffered of the typical problems characterizing such type of data. Inter occlusions, different skin colors among users even of the same ethnic group and unstable illumination conditions, in facts, often made this problem intractable. Other approaches, instead, solved the previous problems by making the user wear sensorized gloves or hold proper tools designed to help the hand localization in the scene. The recent introduction in the mass market of novel low-cost range cameras, like the Microsoft Kinect, Asus XTION, Creative Senz3D, and the Leap Motion, has opened the way to innovative gesture recognition approaches exploiting the geometry of the framed scene. Most methods share a common gesture recognition pipeline based on firstly identifying the hand in the framed scene, then extracting some relevant features on the hand samples and finally exploiting suitable machine learning techniques in order to recognize the performed gesture from a predefined ``gesture dictionary''. This thesis, based on the previous rationale, proposes a novel gesture recognition framework exploiting both color and geometric cues from low-cost color and range cameras. The dissertation starts by introducing the automatic hand gesture recognition problem, giving an overview of the state-of-art algorithms and the recognition pipeline employed in this work. Then, it briefly describes the major low-cost range cameras and setups used in literature for color and depth data acquisition for hand gesture recognition purposes, highlighting their capabilities and limitations. The methods employed for respectively detecting the hand in the framed scene and segmenting it in its relevant parts are then analyzed with a higher level of detail. The algorithm first exploits skin color information and geometrical considerations for discarding the background samples, then it reliably detects the palm and the finger regions, and removes the forearm. For the palm detection, the method fits the largest circle inscribed in the palm region or, in a more advanced version, an ellipse. A set of robust color and geometric features which can be extracted from the fingers and palm regions, previously segmented, is then illustrated accurately. Geometric features describe properties of the hand contour from its curvature variations, the distances in the 3D space or in the image plane of its points from the hand center or from the palm, or extract relevant information from the palm morphology and from the empty space in the hand convex hull. Color features exploit, instead, the histogram of oriented gradients (HOG), local phase quantization (LPQ) and local ternary patterns (LTP) algorithms to provide further helpful cues from the hand texture and the depth map treated as a grayscale image. Additional features extracted from the Leap Motion data complete the gesture characterization for a more reliable recognition. Moreover, the thesis also reports a novel approach jointly exploiting the geometric data provided by the Leap Motion and the depth data from a range camera for extracting the same depth features with a significantly lower computational effort. This work then addresses the delicate problem of constructing a robust gesture recognition model from the features previously described, using multi-class Support Vector Machines, Random Forests or more powerful ensembles of classifiers. Feature selection techniques, designed to detect the smallest subset of features that allow to train a leaner classification model without a significant accuracy loss, are also considered. The proposed recognition method, tested on subsets of the American Sign Language and experimentally validated, reported very high accuracies. The results showed also how higher accuracies are obtainable by combining proper sets of complementary features and using ensembles of classifiers. Moreover, it is worth noticing that the proposed approach is not sensor dependent, that is, the recognition algorithm is not bound to a specific sensor or technology adopted for the depth data acquisition. Eventually, the gesture recognition algorithm is able to run in real-time even in absence of a thorough optimization, and may be easily extended in a near future with novel descriptors and the support for dynamic gestures

An evaluation of depth camera-based hand pose recognition for virtual reality systems.

Masters Degree. University of KwaZulu-Natal, Durban.Camera-based hand gesture recognition for interaction in virtual reality systems promises to provide a more immersive and less distracting means of input than the usual hand-held controllers. It is unknown if a camera would effectively distinguish hand poses made in a virtual reality environment, due to lack of research in this area. This research explores and measures the effectiveness of static hand pose input with a depth camera, specifically the Leap Motion controller, for user interaction in virtual reality applications. A pose set was derived by analyzing existing gesture taxonomies and Leap Motion controller-based virtual reality applications, and a dataset of these poses was constructed using data captured by twenty-five participants. Experiments on the dataset utilizing three popular machine learning classifiers were not able to classify the poses with a high enough accuracy, primarily due to occlusion issues affecting the input data. Therefore, a significantly smaller subset was empirically derived using a novel algorithm, which utilized a confusion matrix from the machine learning experiments as well as a table of Hamming Distances between poses. This improved the recognition accuracy to above 99%, making this set more suitable for real-world use. It is concluded that while camera-based pose recognition can be reliable on a small set of poses, finger occlusion hinders the use of larger sets. Thus, alternative approaches, such as multiple input cameras, should be explored as a potential solution to the occlusion problem

Gesture recognition with application to human-robot interaction

Gestures are a natural form of communication, often transcending language barriers. Recently, much research has been focused on achieving natural human-machine interaction using gestures. This dissertation presents the design of a gestural interface that can be used to control a robot. The system consists of two modes: far-mode and near-mode. In far-mode interaction, upper-body gestures are used to control the motion of a robot. Near-mode interaction uses static hand poses to control a graphical user interface. For upper-body gesture recognition, features are extracted from skeletal data. The extracted features consist of joint angles and relative joint positions and are extracted for each frame of the gesture sequence. A novel key-frame selection algorithm is used to align the gesture sequences temporally. A neural network and hidden Markov model are then used to classify the gestures. The framework was tested on three different datasets, the CMU Military dataset of 3 users, 15 gestures and 10 repetitions per gesture, the VisApp2013 dataset with 28 users, 8 gestures and 1 repetition/gesture and a recorded dataset of 15 users, 10 gestures and 3 repetitions per gesture. The system is shown to achieve a recognition rate of 100% across the three different datasets, using the key-frame selection and a neural network for gesture identification. Static hand-gesture recognition is achieved by first retrieving the 24-DOF hand model. The hand is segmented from the image using both depth and colour information. A novel calibration method is then used to automatically obtain the anthropometric measurements of the user’s hand. The k-curvature algorithm, depth-based and parallel border-based methods are used to detect fingertips in the image. An average detection accuracy of 88% is achieved. A neural network and k-means classifier are then used to classify the static hand gestures. The framework was tested on a dataset of 15 users, 12 gestures and 3 repetitions per gesture. A correct classification rate of 75% is achieved using the neural network. It is shown that the proposed system is robust to changes in skin colour and user hand size

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms