<p>This report examines the problem of real-time head pose estimation in low-resolution football footage. A method is presented for inferring the head pose using a combination of footage and knowledge of the locations of the football and players. An ensemble of randomized ferns is compared with a support vector machine for processing the footage, while a support vector machine performs pattern recognition on the location data. Combining the two sources of information outperforms either in isolation. The location of the football turns out to be an important piece of information.</p> / QC 20100707 / Capturing and Visualizing Large scale Human Action (ACTVIS)

In this text, we present two stereo-based head tracking techniques along with a fast 3D model acquisition system. The first tracking technique is a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user's head under large rotation and illumination variations. Precision and usability of this approach are compared with previous tracking methods for cursor control and target selection in both desktop and interactive room environments. The second tracking technique is designed to improve the robustness of head pose tracking for fast movements. Our iterative hybrid tracker combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. We present experiments which test the accuracy of our approach on sequences of real and synthetic stereo images. The 3D model acquisition system we present quickly aligns intensity and depth images, and reconstructs a textured 3D mesh. 3D views are registered with shape alignment based on our iterative hybrid tracker. We reconstruct the 3D model using a new Cubic Ray Projection merging algorithm which takes advantage of a novel data structure: the linked voxel space. We present experiments to test the accuracy of our approach on 3D face modelling using real-time stereo images.

This paper presents 3-D face modeling with head pose and depth information estimated from a 2-D query face image. Many recent approaches to 3-D face modeling are based on a 3-D morphable model that separately encodes the shape and texture in a parameterized model. The model parameters are often obtained by applying statistical analysis to a set of scanned 3-D faces. Such approaches tend to depend on the number and quality of scanned 3-D faces, which are difficult to obtain and computationally intensive. To overcome the limitations of 3-D morphable models, several modeling techniques from 2-D images have been proposed. We propose a novel framework for depth estimation from a single 2-D image with an arbitrary pose. The proposed scheme uses a set of facial features in a query face image and a reference 3-D face model to estimate the head pose angles of the face. The depth information of the subject at each feature point is represented by the depth information of the reference 3-D face model multiplied by a vector of scale factors. We use the positions of a set of facial feature points on the query 2-D image to deform the reference face dense model into a person specific 3-D face by minimizing an objective function. The objective function is defined as the feature disparity between the facial features in the face image and the corresponding 3-D facial features on the rotated reference model projected onto 2-D space. The pose and depth parameters are iteratively refined until stopping criteria are reached. The proposed method requires only a face image of arbitrary pose for the reconstruction of the corresponding 3-D face dense model with texture. Experiment results with USF Human-ID and Pointing'04 databases show that the proposed approach is effective to estimate depth and head pose information with a single 2-D image. / Electrical and Computer Engineering

This report examines the problem of real-time head pose estimation in low-resolution football footage. A method is presented for inferring the head pose using a combination of footage and knowledge of the locations of the football and players. An ensemble of randomized ferns is compared with a support vector machine for processing the footage, while a support vector machine performs pattern recognition on the location data. Combining the two sources of information outperforms either in isolation. The location of the football turns out to be an important piece of information. / QC 20100707 / Capturing and Visualizing Large scale Human Action (ACTVIS)

No description available.

Submitted by Mariana Dornelles Vargas (marianadv) on 2015-04-27T19:08:59Z No. of bitstreams: 1 deteccao_faces.pdf: 3878917 bytes, checksum: 2fbf8222ef54d5fc0b1df0bf3b3a5292 (MD5) / Made available in DSpace on 2015-04-27T19:08:59Z (GMT). No. of bitstreams: 1 deteccao_faces.pdf: 3878917 bytes, checksum: 2fbf8222ef54d5fc0b1df0bf3b3a5292 (MD5) Previous issue date: 2009-03-20 / HP - Hewlett-Packard Brasil Ltda / As câmeras de vídeo já fazem parte dos novos modelos de interação entre o homem e a máquina. Através destas, a face e a pose da cabeça podem ser detectadas promovendo novos recursos para o usuário. Entre o conjunto de aplicações que têm se beneficiado deste tipo de recurso estão a vídeo-conferência, os jogos educacionais e de entretenimento, o controle de atenção de motoristas e a medida de foco de atenção. Nesse contexto insere-se essa proposta de mestrado, a qual propõe um novo modelo para detectar e rastrear a pose da cabeça a partir de uma seqüência de vídeo obtida com uma câmera monocular. Para alcançar esse objetivo, duas etapas principais foram desenvolvidas: a detecção da face e o rastreamento da pose. Nessa etapa, a face é detectada em pose frontal utilizando-se um detector com haar-like features. Na segunda etapa do algoritmo, após a detecção da face em pose frontal, atributos específicos da mesma são rastreados para estimar a variação da pose de cabeça. / Video cameras are already part of the new man-machine interaction models. Through these, the face and pose of the head can be found, providing new resources for users. Among the applications that have benefited from this type of resource are video conference, educational and entertainment games, and measurement of attention focus. In this context, this Master's thesis proposes a new model to detect and track the pose of the head in a video sequence captured by a monocular camera. To achieve this goal, two main stages were developed: face detection and head pose tracking. The first stage is the starting point for tracking the pose. In this stage, the face is detected in frontal pose using a detector with Haar-like features. In the second step of the algorithm, after detecting the face in frontal pose, specific attributes of the read are tracked to estimate the change in the pose of the head.

Due to the large influx of computers in our daily lives, human-computer interaction has become crucially important. For a long time, focusing on what users need has been critical for designing interaction methods. However, new perspective tends to extend this attitude to encompass how human desires, interests, and ambitions can be met and supported. This implies that the way we interact with computers should be revisited. Centralizing human values rather than user needs is of the utmost importance for providing new interaction techniques. These values drive our decisions and actions, and are essential to what makes us human. This motivated us to introduce new interaction methods that will support human values, particularly human well-being. The aim of this thesis is to design new interaction methods that will empower human to have a healthy, intuitive, and pleasurable interaction with tomorrow’s digital world. In order to achieve this aim, this research is concerned with developing theories and techniques for exploring interaction methods beyond keyboard and mouse, utilizing human body. Therefore, this thesis addresses a very fundamental problem, human motion analysis. Technical contributions of this thesis introduce computer vision-based, marker-less systems to estimate and analyze body motion. The main focus of this research work is on head and hand motion analysis due to the fact that they are the most frequently used body parts for interacting with computers. This thesis gives an insight into the technical challenges and provides new perspectives and robust techniques for solving the problem.

No description available.

During face-to-face meetings, humans use multimodal information, including verbal information, visual information, body language, facial expressions, and other non-verbal gestures. In contrast, during computer-mediated-communication (CMC), humans rely either on mono-modal information such as text-only, voice-only, or video-only or on bi-modal information by using audiovisual modalities such as video teleconferencing. Psychologically, the difference between the two lies in the level of the subjective experience of presence, where people perceive a reduced feeling of presence in the case of CMC. Despite the current advancements in CMC, it is still far from face-to-face communication, especially in terms of the experience of presence. This thesis aims to introduce new concepts, theories, and technologies for presence design where the core is actions for creating presence. Thus, the contribution of the thesis can be divided into a technical contribution and a knowledge contribution. Technically, this thesis details novel technologies for improving presence experience during mediated communication (video teleconferencing). The proposed technologies include action robots (including a telepresence mechatronic robot (TEBoT) and a face robot), embodied control techniques (head orientation modeling and virtual reality headset based collaboration), and face reconstruction/retrieval algorithms. The introduced technologies enable action possibilities and embodied interactions that improve the presence experience between the distantly located participants. The novel setups were put into real experimental scenarios, and the well-known social, spatial, and gaze related problems were analyzed. The developed technologies and the results of the experiments led to the knowledge contribution of this thesis. In terms of knowledge contribution, this thesis presents a more general theoretical conceptual framework for mediated communication technologies. This conceptual framework can guide telepresence researchers toward the development of appropriate technologies for mediated communication applications. Furthermore, this thesis also presents a novel strong concept – presence through actions - that brings in philosophical understandings for developing presence- related technologies. The strong concept - presence through actions is an intermediate-level knowledge that proposes a new way of creating and developing future 'presence artifacts'. Presence- through actions is an action-oriented phenomenological approach to presence that differs from traditional immersive presence approaches that are based (implicitly) on rationalist, internalist views.

Diplomová práce je zaměřena na problematiku detekce a sledování polohy hlavy v obraze jako jednu s možností jak zlepšit možnosti interakce mezi počítačem a člověkem. Hlavním přínosem diplomové práce je využití inovativních hardwarových a softwarových technologií jakými jsou Microsoft Kinect, Point Cloud Library a CImg Library. Na úvod je představeno shrnutí předchozích prací na podobné téma. Následuje charakteristika a popis databáze, která byla vytvořena pro účely diplomové práce. Vyvinutý systém pro detekci a sledování polohy hlavy je založený na akvizici 3D obrazových dat a registračním algoritmu Iterative Closest Point. V závěru diplomové práce je nabídnuto hodnocení vzniklého systému a jsou navrženy možnosti jeho budoucího zlepšení.