12,129 research outputs found

    Improved facial feature fitting for model based coding and animation

    Get PDF
    EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Unobtrusive and pervasive video-based eye-gaze tracking

    Get PDF
    Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

    Thermo-visual feature fusion for object tracking using multiple spatiogram trackers

    Get PDF
    In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework

    Automated Markerless Extraction of Walking People Using Deformable Contour Models

    No full text
    We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people

    Object Tracking and Mensuration in Surveillance Videos

    Get PDF
    This thesis focuses on tracking and mensuration in surveillance videos. The first part of the thesis discusses several object tracking approaches based on the different properties of tracking targets. For airborne videos, where the targets are usually small and with low resolutions, an approach of building motion models for foreground/background proposed in which the foreground target is simplified as a rigid object. For relatively high resolution targets, the non-rigid models are applied. An active contour-based algorithm has been introduced. The algorithm is based on decomposing the tracking into three parts: estimate the affine transform parameters between successive frames using particle filters; detect the contour deformation using a probabilistic deformation map, and regulate the deformation by projecting the updated model onto a trained shape subspace. The active appearance Markov chain (AAMC). It integrates a statistical model of shape, appearance and motion. In the AAMC model, a Markov chain represents the switching of motion phases (poses), and several pairwise active appearance model (P-AAM) components characterize the shape, appearance and motion information for different motion phases. The second part of the thesis covers video mensuration, in which we have proposed a heightmeasuring algorithm with less human supervision, more flexibility and improved robustness. From videos acquired by an uncalibrated stationary camera, we first recover the vanishing line and the vertical point of the scene. We then apply a single view mensuration algorithm to each of the frames to obtain height measurements. Finally, using the LMedS as the cost function and the Robbins-Monro stochastic approximation (RMSA) technique to obtain the optimal estimate

    Computer analysis of objects’ movement in image sequences: methods and applications

    Get PDF
    Computer analysis of objects’ movement in image sequences is a very complex problem, considering that it usually involves tasks for automatic detection, matching, tracking, motion analysis and deformation estimation. In spite of its complexity, this computational analysis has a wide range of important applications; for instance, in surveillance systems, clinical analysis of human gait, objects recognition, pose estimation and deformation analysis. Due to the extent of the purposes, several difficulties arise, such as the simultaneous tracking of manifold objects, their possible temporary occlusion or definitive disappearance from the image scene, changes of the viewpoints considered in images acquisition or of the illumination conditions, or even nonrigid deformations that objects may suffer in image sequences. In this paper, we present an overview of several methods that may be considered to analyze objects’ movement; namely, for their segmentation, tracking and matching in images, and for estimation of the deformation involved between images.This paper was partially done in the scope of project “Segmentation, Tracking and Motion Analysis of Deformable (2D/3D) Objects using Physical Principles”, with reference POSC/EEA-SRI/55386/2004, financially supported by FCT -Fundação para a CiĂȘncia e a Tecnologia from Portugal. The fourth, fifth and seventh authors would like to thank also the support of their PhD grants from FCT with references SFRH/BD/29012/2006, SFRH/BD/28817/2006 and SFRH/BD/12834/2003, respectively

    Real-time systems for moving objects detection and tracking using pixel difference method.

    Get PDF

    Real time hand gesture recognition including hand segmentation and tracking

    Get PDF
    In this paper we present a system that performs automatic gesture recognition. The system consists of two main components: (i) A unified technique for segmentation and tracking of face and hands using a skin detection algorithm along with handling occlusion between skin objects to keep track of the status of the occluded parts. This is realized by combining 3 useful features, namely, color, motion and position. (ii) A static and dynamic gesture recognition system. Static gesture recognition is achieved using a robust hand shape classification, based on PCA subspaces, that is invariant to scale along with small translation and rotation transformations. Combining hand shape classification with position information and using DHMMs allows us to accomplish dynamic gesture recognition
    • 

    corecore