396 research outputs found

    Deep Learning for Head Pose Estimation: A Survey

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    Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic

    3D hand pose estimation using convolutional neural networks

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    3D hand pose estimation plays a fundamental role in natural human computer interactions. The problem is challenging due to complicated variations caused by complex articulations, multiple viewpoints, self-similar parts, severe self-occlusions, different shapes and sizes. To handle these challenges, the thesis makes the following contributions. First, the problem of the multiple viewpoints and complex articulations of hand pose estimation is tackled by decomposing and transforming the input and output space by spatial transformations following the hand structure. By the transformation, both the variation of the input space and output is reduced, which makes the learning easier. The second contribution is a probabilistic framework integrating all the hierarchical regressions. Variants with/without sampling, using different regressors and optimization methods are constructed and compared to provide an insight of the components under this framework. The third contribution is based on the observation that for images with occlusions, there exist multiple plausible configurations for the occluded parts. A hierarchical mixture density network is proposed to handle the multi-modality of the locations for occluded hand joints. It leverages the state-of-the-art hand pose estimators based on Convolutional Neural Networks to facilitate feature learning while models the multiple modes in a two-level hierarchy to reconcile single-valued (for visible joints) and multi-valued (for occluded joints) mapping in its output. In addition, a complete labeled real hand datasets is collected by a tracking system with six 6D magnetic sensors and inverse kinematics to automatically obtain 21-joints hand pose annotations of depth maps.Open Acces

    Three-dimensional model-based analysis of vascular and cardiac images

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    This thesis is concerned with the geometrical modeling of organs to perform medical image analysis tasks. The thesis is divided in two main parts devoted to model linear vessel segments and the left ventricle of the heart, respectively. Chapters 2 to 4 present different aspects of a model-based technique for semi-automated quantification of linear vessel segments from 3-D Magnetic Resonance Angiography (MRA). Chapter 2 is concerned with a multiscale filter for the enhancement of vessels in 2-D and 3-D angiograms. Chapter 3 applies the filter developed in Chapter 2 to determine the central vessel axis in 3-D MRA images. This procedure is initialized using an efficient user interaction technique that naturally incorporates the knowledge of the operator about the vessel of interest. Also in this chapter, a linear vessel model is used to recover the position of the vessel wall in order to carry out an accurate quantitative analysis of vascular morphology. Prior knowledge is provided in two main forms: a cylindrical model introduces a shape prior while prior knowledge on the image acquisition (type of MRA technique) is used to define an appropriate vessel boundary criterion. In Chapter 4 an extensive in vitro and in vivo evaluation of the algorithm introduced in Chapter 3 is described. Chapters 5 to 7 change the focus to 3D cardiac image analysis from Magnetic Resonance Imaging. Chapter 5 presents an extensive survey, a categorization and a critical review of the field of cardiac modeling. Chapter 6 and Chapter 7 present successive refinements of a method for building statistical models of shape variability with particular emphasis on cardiac modeling. The method is based on an elastic registration method using hierarchical free-form deformations. A 3D shape model of the left and right ventricles of the heart was constructed. This model contains both the average shape of these organs as well as their shape variability. The methodology presented in the last two chapters could also be applied to other anatomical structures. This has been illustrated in Chapter 6 with examples of geometrical models of the nucleus caudate and the radius

    Single and multiple object tracking using a multi-feature joint sparse representation

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    In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms

    Computationally efficient deformable 3D object tracking with a monocular RGB camera

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    182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices

    Computationally efficient deformable 3D object tracking with a monocular RGB camera

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    182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices
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