Real-Time Kinect Fingers Tracking and Recognition for Servo Gripper Controller

The conventional method to control something like machines such as remote controllers or wearable sensors have its limitation and cannot cater the high demands in some scenarios. To overcome this situation, a real-time Kinect finger tracking and recognition is developed to control a servo gripper via a single board microcontroller, Arduino. This paper presents hand and finger tracking method that can determine the number of fingers and also the angle of the finger’s position. The hand gesture is captured by the Kinect and go through a series of image processing and finally, the information will send to the Arduino. The image processing method includes a detection of hand using depth sensor on the Kinect, then finds the finger and calculates the angle of the finger’s position. The convex hull algorithm is used to represent the region of the hand. The fingertips are recognized by calculating the angle of the fingertip and each angle is compared with threshold angle. The orientation and position of the fingers are obtained by finding the middle line of the finger and compares to the vertical line of the middle finger, then calculates the angle. The result shows that the number of fingers appeared in the display can be recognized and the orientation and position of each finger can be determined. A gripper also able to react simultaneously (open and close) based on the detected finger

Remote control of a robotic hand using a leap sensor

This paper presents a low-cost gesture-based remote control of a ro-botic hand. The proposed control architecture is based on a commercial leap motion sensor and an Arduino board, which have been chosen due to their low-cost and user-friendly features. A specific Matlab code has been implemented to collect data from the leap motion sensor and to generate proper instructions to control a robotic hand, which has been 3D print at Sheffield Hallam Univer-sity. Experimental tests have been carried out validate the effectiveness of the proposed remote control for performing various grasping tasks

Understanding egocentric human actions with temporal decision forests

Understanding human actions is a fundamental task in computer vision with a wide range of applications including pervasive health-care, robotics and game control. This thesis focuses on the problem of egocentric action recognition from RGB-D data, wherein the world is viewed through the eyes of the actor whose hands describe the actions. The main contributions of this work are its findings regarding egocentric actions as described by hands in two application scenarios and a proposal of a new technique that is based on temporal decision forests. The thesis first introduces a novel framework to recognise fingertip writing in mid-air in the context of human-computer interaction. This framework detects whether the user is writing and tracks the fingertip over time to generate spatio-temporal trajectories that are recognised by using a Hough forest variant that encourages temporal consistency in prediction. A problem with using such forest approach for action recognition is that the learning of temporal dynamics is limited to hand-crafted temporal features and temporal regression, which may break the temporal continuity and lead to inconsistent predictions. To overcome this limitation, the thesis proposes transition forests. Besides any temporal information that is encoded in the feature space, the forest automatically learns the temporal dynamics during training, and it is exploited in inference in an online and efficient manner achieving state-of-the-art results. The last contribution of this thesis is its introduction of the first RGB-D benchmark to allow for the study of egocentric hand-object actions with both hand and object pose annotations. This study conducts an extensive evaluation of different baselines, state-of-the art approaches and temporal decision forest models using colour, depth and hand pose features. Furthermore, it extends the transition forest model to incorporate data from different modalities and demonstrates the benefit of using hand pose features to recognise egocentric human actions. The thesis concludes by discussing and analysing the contributions and proposing a few ideas for future work.Open Acces

Human body analysis using depth data

Human body analysis is one of the broadest areas within the computer vision field. Researchers have put a strong effort in the human body analysis area, specially over the last decade, due to the technological improvements in both video cameras and processing power. Human body analysis covers topics such as person detection and segmentation, human motion tracking or action and behavior recognition. Even if human beings perform all these tasks naturally, they build-up a challenging problem from a computer vision point of view. Adverse situations such as viewing perspective, clutter and occlusions, lighting conditions or variability of behavior amongst persons may turn human body analysis into an arduous task. In the computer vision field, the evolution of research works is usually tightly related to the technological progress of camera sensors and computer processing power. Traditional human body analysis methods are based on color cameras. Thus, the information is extracted from the raw color data, strongly limiting the proposals. An interesting quality leap was achieved by introducing the multiview concept. That is to say, having multiple color cameras recording a single scene at the same time. With multiview approaches, 3D information is available by means of stereo matching algorithms. The fact of having 3D information is a key aspect in human motion analysis, since the human body moves in a three-dimensional space. Thus, problems such as occlusion and clutter may be overcome with 3D information. The appearance of commercial depth cameras has supposed a second leap in the human body analysis field. While traditional multiview approaches required a cumbersome and expensive setup, as well as a fine camera calibration; novel depth cameras directly provide 3D information with a single camera sensor. Furthermore, depth cameras may be rapidly installed in a wide range of situations, enlarging the range of applications with respect to multiview approaches. Moreover, since depth cameras are based on infra-red light, they do not suffer from illumination variations. In this thesis, we focus on the study of depth data applied to the human body analysis problem. We propose novel ways of describing depth data through specific descriptors, so that they emphasize helpful characteristics of the scene for further body analysis. These descriptors exploit the special 3D structure of depth data to outperform generalist 3D descriptors or color based ones. We also study the problem of person detection, proposing a highly robust and fast method to detect heads. Such method is extended to a hand tracker, which is used throughout the thesis as a helpful tool to enable further research. In the remainder of this dissertation, we focus on the hand analysis problem as a subarea of human body analysis. Given the recent appearance of depth cameras, there is a lack of public datasets. We contribute with a dataset for hand gesture recognition and fingertip localization using depth data. This dataset acts as a starting point of two proposals for hand gesture recognition and fingertip localization based on classification techniques. In these methods, we also exploit the above mentioned descriptor proposals to finely adapt to the nature of depth data.%, and enhance the results in front of traditional color-based methods.L’anàlisi del cos humà és una de les àrees més àmplies del camp de la visió per computador. Els investigadors han posat un gran esforç en el camp de l’anàlisi del cos humà, sobretot durant la darrera dècada, degut als grans avenços tecnològics, tant pel que fa a les càmeres com a la potencia de càlcul. L’anàlisi del cos humà engloba varis temes com la detecció i segmentació de persones, el seguiment del moviment del cos, o el reconeixement d'accions. Tot i que els essers humans duen a terme aquestes tasques d'una manera natural, es converteixen en un difícil problema quan s'ataca des de l’òptica de la visió per computador. Situacions adverses, com poden ser la perspectiva del punt de vista, les oclusions, les condicions d’il•luminació o la variabilitat de comportament entre persones, converteixen l’anàlisi del cos humà en una tasca complicada. En el camp de la visió per computador, l’evolució de la recerca va sovint lligada al progrés tecnològic, tant dels sensors com de la potencia de càlcul dels ordinadors. Els mètodes tradicionals d’anàlisi del cos humà estan basats en càmeres de color. Això limita molt els enfocaments, ja que la informació disponible prové únicament de les dades de color. El concepte multivista va suposar salt de qualitat important. En els enfocaments multivista es tenen múltiples càmeres gravant una mateixa escena simultàniament, permetent utilitzar informació 3D gràcies a algorismes de combinació estèreo. El fet de disposar d’informació 3D es un punt clau, ja que el cos humà es mou en un espai tri-dimensional. Això doncs, problemes com les oclusions es poden apaivagar si es disposa de informació 3D. L’aparició de les càmeres de profunditat comercials ha suposat un segon salt en el camp de l’anàlisi del cos humà. Mentre els mètodes multivista tradicionals requereixen un muntatge pesat i car, i una celebració precisa de totes les càmeres; les noves càmeres de profunditat ofereixen informació 3D de forma directa amb un sol sensor. Aquestes càmeres es poden instal•lar ràpidament en una gran varietat d'entorns, ampliant enormement l'espectre d'aplicacions, que era molt reduït amb enfocaments multivista. A més a més, com que les càmeres de profunditat estan basades en llum infraroja, no pateixen problemes relacionats amb canvis d’il•luminació. En aquesta tesi, ens centrem en l'estudi de la informació que ofereixen les càmeres de profunditat, i la seva aplicació al problema d’anàlisi del cos humà. Proposem noves vies per descriure les dades de profunditat mitjançant descriptors específics, capaços d'emfatitzar característiques de l'escena que seran útils de cara a una posterior anàlisi del cos humà. Aquests descriptors exploten l'estructura 3D de les dades de profunditat per superar descriptors 3D generalistes o basats en color. També estudiem el problema de detecció de persones, proposant un mètode per detectar caps robust i ràpid. Ampliem aquest mètode per obtenir un algorisme de seguiment de mans que ha estat utilitzat al llarg de la tesi. En la part final del document, ens centrem en l’anàlisi de les mans com a subàrea de l’anàlisi del cos humà. Degut a la recent aparició de les càmeres de profunditat, hi ha una manca de bases de dades públiques. Contribuïm amb una base de dades pensada per la localització de dits i el reconeixement de gestos utilitzant dades de profunditat. Aquesta base de dades és el punt de partida de dues contribucions sobre localització de dits i reconeixement de gestos basades en tècniques de classificació. En aquests mètodes, també explotem les ja mencionades propostes de descriptors per millor adaptar-nos a la naturalesa de les dades de profunditat

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