Resolving Multi-path Interference in Time-of-Flight Imaging via Modulation Frequency Diversity and Sparse Regularization

Time-of-flight (ToF) cameras calculate depth maps by reconstructing phase shifts of amplitude-modulated signals. For broad illumination or transparent objects, reflections from multiple scene points can illuminate a given pixel, giving rise to an erroneous depth map. We report here a sparsity regularized solution that separates K-interfering components using multiple modulation frequency measurements. The method maps ToF imaging to the general framework of spectral estimation theory and has applications in improving depth profiles and exploiting multiple scattering.Comment: 11 Pages, 4 figures, appeared with minor changes in Optics Letter

Portuguese sign language recognition via computer vision and depth sensor

Sign languages are used worldwide by a multitude of individuals. They are mostly used by the deaf communities and their teachers, or people associated with them by ties of friendship or family. Speakers are a minority of citizens, often segregated, and over the years not much attention has been given to this form of communication, even by the scientific community. In fact, in Computer Science there is some, but limited, research and development in this area. In the particular case of sign Portuguese Sign Language-PSL that fact is more evident and, to our knowledge there isn’t yet an efficient system to perform the automatic recognition of PSL signs. With the advent and wide spreading of devices such as depth sensors, there are new possibilities to address this problem. In this thesis, we have specified, developed, tested and preliminary evaluated, solutions that we think will bring valuable contributions to the problem of Automatic Gesture Recognition, applied to Sign Languages, such as the case of Portuguese Sign Language. In the context of this work, Computer Vision techniques were adapted to the case of Depth Sensors. A proper gesture taxonomy for this problem was proposed, and techniques for feature extraction, representation, storing and classification were presented. Two novel algorithms to solve the problem of real-time recognition of isolated static poses were specified, developed, tested and evaluated. Two other algorithms for isolated dynamic movements for gesture recognition (one of them novel), have been also specified, developed, tested and evaluated. Analyzed results compare well with the literature.As Línguas Gestuais são utilizadas em todo o Mundo por uma imensidão de indivíduos. Trata-se na sua grande maioria de surdos e/ou mudos, ou pessoas a eles associados por laços familiares de amizade ou professores de Língua Gestual. Tratando-se de uma minoria, muitas vezes segregada, não tem vindo a ser dada ao longo dos anos pela comunidade científica, a devida atenção a esta forma de comunicação. Na área das Ciências da Computação existem alguns, mas poucos trabalhos de investigação e desenvolvimento. No caso particular da Língua Gestual Portuguesa - LGP esse facto é ainda mais evidente não sendo nosso conhecimento a existência de um sistema eficaz e efetivo para fazer o reconhecimento automático de gestos da LGP. Com o aparecimento ou massificação de dispositivos, tais como sensores de profundidade, surgem novas possibilidades para abordar este problema. Nesta tese, foram especificadas, desenvolvidas, testadas e efectuada a avaliação preliminar de soluções que acreditamos que trarão valiosas contribuições para o problema do Reconhecimento Automático de Gestos, aplicado às Línguas Gestuais, como é o caso da Língua Gestual Portuguesa. Foram adaptadas técnicas de Visão por Computador ao caso dos Sensores de Profundidade. Foi proposta uma taxonomia adequada ao problema, e apresentadas técnicas para a extração, representação e armazenamento de características. Foram especificados, desenvolvidos, testados e avaliados dois algoritmos para resolver o problema do reconhecimento em tempo real de poses estáticas isoladas. Foram também especificados, desenvolvidos, testados e avaliados outros dois algoritmos para o Reconhecimento de Movimentos Dinâmicos Isolados de Gestos(um deles novo).Os resultados analisados são comparáveis à literatura.Las lenguas de Signos se utilizan en todo el Mundo por una multitud de personas. En su mayoría son personas sordas y/o mudas, o personas asociadas con ellos por vínculos de amistad o familiares y profesores de Lengua de Signos. Es una minoría de personas, a menudo segregadas, y no se ha dado en los últimos años por la comunidad científica, la atención debida a esta forma de comunicación. En el área de Ciencias de la Computación hay alguna pero poca investigación y desarrollo. En el caso particular de la Lengua de Signos Portuguesa - LSP, no es de nuestro conocimiento la existencia de un sistema eficiente y eficaz para el reconocimiento automático. Con la llegada en masa de dispositivos tales como Sensores de Profundidad, hay nuevas posibilidades para abordar el problema del Reconocimiento de Gestos. En esta tesis se han especificado, desarrollado, probado y hecha una evaluación preliminar de soluciones, aplicada a las Lenguas de Signos como el caso de la Lengua de Signos Portuguesa - LSP. Se han adaptado las técnicas de Visión por Ordenador para el caso de los Sensores de Profundidad. Se propone una taxonomía apropiada para el problema y se presentan técnicas para la extracción, representación y el almacenamiento de características. Se desarrollaran, probaran, compararan y analizan los resultados de dos nuevos algoritmos para resolver el problema del Reconocimiento Aislado y Estático de Posturas. Otros dos algoritmos (uno de ellos nuevo) fueran también desarrollados, probados, comparados y analizados los resultados, para el Reconocimiento de Movimientos Dinámicos Aislados de los Gestos

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

Towards sociable virtual humans : multimodal recognition of human input and behavior

One of the biggest obstacles for constructing effective sociable virtual humans lies in the failure of machines to recognize the desires, feelings and intentions of the human user. Virtual humans lack the ability to fully understand and decode the communication signals human users emit when communicating with each other. This article describes our research in overcoming this problem by developing senses for the virtual humans which enables them to hear and understand human speech, localize the human user in front of the display system, recognize hand postures and to recognize the emotional state of the human user by classifying facial expression. We report on the methods needed to perform these tasks in real-time and conclude with an outlook on promising research issues of the future

Real-time motion-based hand gestures recognition from time-of-flight video

The final publication is available at Springer via http://dx.doi.org/10.1007/s11265-015-1090-5This paper presents an innovative solution based on Time-Of-Flight (TOF) video technology to motion patterns detection for real-time dynamic hand gesture recognition. The resulting system is able to detect motion-based hand gestures getting as input depth images. The recognizable motion patterns are modeled on the basis of the human arm anatomy and its degrees of freedom, generating a collection of synthetic motion patterns that is compared with the captured input patterns in order to finally classify the input gesture. For the evaluation of our system a significant collection of gestures has been compiled, getting results for 3D pattern classification as well as a comparison with the results using only 2D informatio

Ontological representation of time-of-flight camera data to support vision-based AmI

Proceedings of: 4th International Workshop on Sensor Networks and Ambient Intelligence, 19-23 March 2012, Lugano ( Switzerland)Recent advances in technologies for capturing video data have opened a vast amount of new application areas. Among them, the incorporation of Time-of-Flight (ToF) cameras on Ambient Intelligence (AmI) environments. Although theperformance of tracking algorithms have quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted for smart environments. This paper presents an extension of a previous system in the area of videobased AmI to incorporate ToF information to enhance sceneinterpretation. The framework is founded on an ontologybased model of the scene, which is extended to incorporate ToF data. The advantages and new features of the model are demonstrated in a Social Signal Processing (SSP) application.This work was supported in part by Projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029- C02-02.Publicad

Towards Naturalistic Interfaces of Virtual Reality Systems

Interaction plays a key role in achieving realistic experience in virtual reality (VR). Its realization depends on interpreting the intents of human motions to give inputs to VR systems. Thus, understanding human motion from the computational perspective is essential to the design of naturalistic interfaces for VR. This dissertation studied three types of human motions, including locomotion (walking), head motion and hand motion in the context of VR. For locomotion, the dissertation presented a machine learning approach for developing a mechanical repositioning technique based on a 1-D treadmill for interacting with a unique new large-scale projective display, called the Wide-Field Immersive Stereoscopic Environment (WISE). The usability of the proposed approach was assessed through a novel user study that asked participants to pursue a rolling ball at variable speed in a virtual scene. In addition, the dissertation studied the role of stereopsis in avoiding virtual obstacles while walking by asking participants to step over obstacles and gaps under both stereoscopic and non-stereoscopic viewing conditions in VR experiments. In terms of head motion, the dissertation presented a head gesture interface for interaction in VR that recognizes real-time head gestures on head-mounted displays (HMDs) using Cascaded Hidden Markov Models. Two experiments were conducted to evaluate the proposed approach. The first assessed its offline classification performance while the second estimated the latency of the algorithm to recognize head gestures. The dissertation also conducted a user study that investigated the effects of visual and control latency on teleoperation of a quadcopter using head motion tracked by a head-mounted display. As part of the study, a method for objectively estimating the end-to-end latency in HMDs was presented. For hand motion, the dissertation presented an approach that recognizes dynamic hand gestures to implement a hand gesture interface for VR based on a static head gesture recognition algorithm. The proposed algorithm was evaluated offline in terms of its classification performance. A user study was conducted to compare the performance and the usability of the head gesture interface, the hand gesture interface and a conventional gamepad interface for answering Yes/No questions in VR. Overall, the dissertation has two main contributions towards the improvement of naturalism of interaction in VR systems. Firstly, the interaction techniques presented in the dissertation can be directly integrated into existing VR systems offering more choices for interaction to end users of VR technology. Secondly, the results of the user studies of the presented VR interfaces in the dissertation also serve as guidelines to VR researchers and engineers for designing future VR systems

Exploitation of time-of-flight (ToF) cameras

This technical report reviews the state-of-the art in the field of ToF cameras, their advantages, their limitations, and their present-day applications sometimes in combination with other sensors. Even though ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight and reduced power consumption have motivated their use in numerous areas of research. In robotics, these areas range from mobile robot navigation and map building to vision-based human motion capture and gesture recognition, showing particularly a great potential in object modeling and recognition.Preprin

A hybrid method using kinect depth and color data stream for hand blobs segmentation

The recently developed depth sensors such as Kinect have provided new potential for human-computer interaction (HCI) and hand gesture are one of main parts in recent researches. Hand segmentation procedure is performed to acquire hand gesture from a captured image. In this paper, a method is produced to segment hand blobs using both depth and color data frames. This method applies a body segmentation and an image threshold techniques to depth data frame using skeleton data and concurrently it uses SLIC super-pixel segmentation method to extract hand blobs from color data frame with the help of skeleton data. The proposed method has low computation time and shows significant results when basic assumption are fulfilled