Relevant bibliographies by topics / 3D Human Pose Estimation / Dissertations / Theses
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Dissertations / Theses on the topic '3D Human Pose Estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Budaraju, Sri Datta. "Unsupervised 3D Human Pose Estimation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291435.

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The thesis proposes an unsupervised representation learning method to predict 3D human pose from a 2D skeleton via a VAEGAN (Variational Autoencoder Generative Adversarial Network) hybrid network. The method learns to lift poses from 2D to 3D using selfsupervision and adversarial learning techniques. The method does not use images, heatmaps, 3D pose annotations, paired/unpaired 2Dto3D skeletons, 3D priors, synthetic 2D skeletons, multiview or temporal information in any shape or form. The 2D skeleton input is taken by a VAE that encodes it in a latent space and then decodes that latent represe
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2

Wang, Jianquan. "A Human Kinetic Dataset and a Hybrid Model for 3D Human Pose Estimation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41437.

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Human pose estimation represents the skeleton of a person in color or depth images to improve a machine’s understanding of human movement. 3D human pose estimation uses a three-dimensional skeleton to represent the human body posture, which is more stereoscopic than a two-dimensional skeleton. Therefore, 3D human pose estimation can enable machines to play a role in physical education and health recovery, reducing labor costs and the risk of disease transmission. However, the existing datasets for 3D pose estimation do not involve fast motions that would cause optical blur for a monocular came
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3

Gong, Wenjuan. "3D Motion Data aided Human Action Recognition and Pose Estimation." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/116189.

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En aquest treball s’explora el reconeixement d’accions humanes i l'estimació de la seva postura en seqüències d'imatges. A diferència de les tècniques tradicionals d’aprenentatge a partir d’imatges 2D o vídeo amb la sortida anotada, en aquesta Tesi abordem aquest objectiu amb la informació de moviment 3D capturat, que ens ajudar a tancar el llaç entre les característiques 2D de la imatge i les interpretacions sobre el moviment humà.<br>En este trabajo se exploran el reconocimiento de acciones humanas y la estimación de su postura en secuencias de imágenes. A diferencia de las técnicas tradicio
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4

Yu, Tsz-Ho. "Classification and pose estimation of 3D shapes and human actions." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708443.

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5

Darby, John. "3D Human Motion Tracking and Pose Estimation using Probabilistic Activity Models." Thesis, Manchester Metropolitan University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523145.

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This thesis presents work on generative approaches to human motion tracking and pose estimation where a geometric model of the human body is used for comparison with observations. The existing generative tracking literature can be quite clearly divided between two groups. First, approaches that attempt to solve a difficult high-dimensional inference problem in the body model's full or ambient pose space, recovering freeform or unknown activity. Second, approaches that restrict inference to a low-dimensional latent embedding of the full pose space, recovering activity for which training data is
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6

Borodulina, A. (Anastasiia). "Application of 3D human pose estimation for motion capture and character animation." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906262670.

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Abstract. Interest in motion capture (mocap) technology is growing every day, and the number of possible applications is multiplying. But such systems are very expensive and are not affordable for personal use. Based on that, this thesis presents the framework that can produce mocap data from regular RGB video and then use it to animate a 3D character according to the movement of the person in the original video. To extract the mocap data from the input video, one of the three 3D pose estimation (PE) methods that are available within the scope of the project is used to determine where the join
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7

Burenius, Magnus. "Human 3D Pose Estimation in the Wild : using Geometrical Models and Pictorial Structures." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138136.

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8

Mehta, Dushyant [Verfasser]. "Real-time 3D human body pose estimation from monocular RGB input / Dushyant Mehta." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1220691135/34.

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9

Norman, Jacob. "3D POSE ESTIMATION IN THE CONTEXT OF GRIP POSITION FOR PHRI." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55166.

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For human-robot interaction with the intent to grip a human arm, it is necessary that the ideal gripping location can be identified. In this work, the gripping location is situated on the arm and thus it can be extracted using the position of the wrist and elbow joints. To achieve this human pose estimation is proposed as there exist robust methods that work both in and outside of lab environments. One such example is OpenPose which thanks to the COCO and MPII datasets has recorded impressive results in a variety of different scenarios in real-time. However, most of the images in these dataset
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Fathollahi, Ghezelghieh Mona. "Estimation of Human Poses Categories and Physical Object Properties from Motion Trajectories." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6835.

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Despite the impressive advancements in people detection and tracking, safety is still a key barrier to the deployment of autonomous vehicles in urban environments [1]. For example, in non-autonomous technology, there is an implicit communication between the people crossing the street and the driver to make sure they have communicated their intent to the driver. Therefore, it is crucial for the autonomous car to infer the future intent of the pedestrian quickly. We believe that human body orientation with respect to the camera can help the intelligent unit of the car to anticipate the future mo
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11

Hossain, Mir Rayat Imtiaz. "Understanding the sources of error for 3D human pose estimation from monocular images and videos." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/63808.

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With the success of deep learning in the field of computer vision, most state-of-the-art approaches of estimating 3D human pose from images or videos rely on training a network end-to-end which can regress into 3D joint locations or heatmaps from an RGB image. Although most of these approaches provide good results, the major sources of error are often difficult to understand. The errors may either come from incorrect 2D pose estimation or from the incorrect mapping of the features in 2D to 3D. In this work, we aim to understand the sources of error in estimating 3D pose from images and videos.
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12

SARMADI, HAMID. "Human Detection and Pose Estimation in aMulti-camera System : Using a 3D version of Pictorial Structures." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142372.

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Multi-view 3D human pose estimation is still a challenging task in uncontrolled environments. Many related approaches are still dependent on silhouettes of the subject obtained by background subtraction. Background subtraction is difficult if the cameras are moving or the background is dynamic. Our solution is to do body part detections both on the foreground and the background, similar to some 2D pose estimation algorithms. We introduce a novel approach to combine 2D body part detections from multiple views to obtain a unified space of detection scores employing the constraints of the 3D spac
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13

Carbonera, Luvizon Diogo. "Apprentissage automatique pour la reconnaissance d'action humaine et l'estimation de pose à partir de l'information 3D." Thesis, Cergy-Pontoise, 2019. http://www.theses.fr/2019CERG1015.

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La reconnaissance d'actions humaines en 3D est une tâche difficile en raisonde la complexité de mouvements humains et de la variété des poses et desactions accomplies par différents sujets. Les technologies récentes baséessur des capteurs de profondeur peuvent fournir les représentationssquelettiques à faible coût de calcul, ce qui est une information utilepour la reconnaissance d'actions.Cependant, ce type de capteurs se limite à des environnementscontrôlés et génère fréquemment des données bruitées. Parallèlement à cesavancées technologiques, les réseaux de neurones convolutifs (CNN) ontmont
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Benzine, Abdallah. "Estimation de poses 3D multi-personnes à partir d'images RGB." Thesis, Sorbonne université, 2020. http://www.theses.fr/2020SORUS103.

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L’estimation de poses 3D humaines à partir d’images RGB monoculaires est le processus permettant de localiser les articulations humaines à partir d’une image ou d’une séquence d’images. Elle fournit une information géométrique et de mouvement riche sur le corps humain. La plus part des approches d’estimation de poses 3D existantes supposent que l’image ne contient qu’une personne, entièrement visible. Un tel scénario n’est pas réaliste. Dans des conditions réelles plusieurs personnes interagissent. Elles ont alors tendance à s’occulter mutuellement, ce qui rend l’estimation de poses 3D encore
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15

Duncan, Kester. "Scene-Dependent Human Intention Recognition for an Assistive Robotic System." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5009.

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In order for assistive robots to collaborate effectively with humans for completing everyday tasks, they must be endowed with the ability to effectively perceive scenes and more importantly, recognize human intentions. As a result, we present in this dissertation a novel scene-dependent human-robot collaborative system capable of recognizing and learning human intentions based on scene objects, the actions that can be performed on them, and human interaction history. The aim of this system is to reduce the amount of human interactions necessary for communicating tasks to a robot. Accordingly,
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16

Jack, Dominic. "Deep learning approaches for 3D inference from monocular vision." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/204267/1/Dominic_Jack_Thesis.pdf.

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This thesis looks at deep learning approaches to 3D computer vision problems, using representations including occupancy grids, deformable meshes, key points, point clouds, and event streams. We focussed on methods targeted towards medium-sized mobile robotics platforms with modest computational power on board. Key results include state-of-the-art accuracies on single-view high resolution voxel reconstruction and event camera classification tasks, point cloud convolution networks capable of performing inference an order of magnitude faster than similar methods, and a 3D human pose lifting model
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Regia, Corte Fabiola. "Studio ed implementazione di un modello di Human Pose Estimation 3D. Analisi tecnica della posizione del corpo dell’atleta durante un match di Tennis." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Al giorno d’oggi, senza esserne troppo consapevoli, il Machine Learning sta entrando a far parte dei più svariati settori, professionali o privati che siano; dalla Classificazione in ambito agricolo alle auto a guida autonoma; dal Riconoscimento del parlato in ambito didattico all’individuazione di oggetti in un paesaggio, fino a giungere all’ambito sportivo, che sia individuale o di squadra, di livello amatoriale o professionistico. Ed è proprio in quest’ultimo ambito che si colloca questo progetto: tratteremo infatti l’utilizzo delle reti convoluzionali per la stima della posa umana in ambit
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Amin, Sikandar [Verfasser], Bernd [Akademischer Betreuer] Radig, Darius [Gutachter] Burschka, and Bernd [Gutachter] Radig. "Multi-view Part-based Models for 3D Human Pose Estimation in Real-World Scenes / Sikandar Amin ; Gutachter: Darius Burschka, Bernd Radig ; Betreuer: Bernd Radig." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1171425422/34.

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19

Rydén, Anna, and Amanda Martinsson. "Evaluation of 3D motion capture data from a deep neural network combined with a biomechanical model." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176543.

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Motion capture has in recent years grown in interest in many fields from both game industry to sport analysis. The need of reflective markers and expensive multi-camera systems limits the business since they are costly and time-consuming. One solution to this could be a deep neural network trained to extract 3D joint estimations from a 2D video captured with a smartphone. This master thesis project has investigated the accuracy of a trained convolutional neural network, MargiPose, that estimates 25 joint positions in 3D from a 2D video, against a gold standard, multi-camera Vicon-system. The p
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Xu, Wanxin. "AFFECT-PRESERVING VISUAL PRIVACY PROTECTION." UKnowledge, 2018. https://uknowledge.uky.edu/ece_etds/122.

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The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behavior
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Zendjebil, Imane. "Localisation 3D basée sur une approche de suppléance multi-capteurs pour la Réalité Augmentée Mobile en Milieu Extérieur." Phd thesis, Université d'Evry-Val d'Essonne, 2010. http://tel.archives-ouvertes.fr/tell-00541366.

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La démocratisation des terminaux mobiles telle que les téléphones cellulaires, les PDAs et les tablettes PC a rendu possible le déploiement de la réalité augmentée dans des environnements en extérieur à grande échelle. Cependant, afin de mettre en oeuvre de tels systèmes, différentes problématiques doivent êtres traitées. Parmi elle, la localisation représente l?une des plus importantes. En effet, l?estimation de la position et de l?orientation (appelée pose) du point de vue (de la caméra ou de l?utilisateur) permet de recaler les objets virtuels sur les parties observées de la scène réelle. D
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Blanc, Beyne Thibault. "Estimation de posture 3D à partir de données imprécises et incomplètes : application à l'analyse d'activité d'opérateurs humains dans un centre de tri." Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0106.

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Dans un contexte d’étude de la pénibilité et de l’ergonomie au travail pour la prévention des troubles musculo-squelettiques, la société Ebhys cherche à développer un outil d’analyse de l’activité des opérateurs humains dans un centre de tri, par l’évaluation d’indicateurs ergonomiques. Pour faire face à l’environnement non contrôlé du centre de tri et pour faciliter l’acceptabilité du dispositif, ces indicateurs sont mesurés à partir d’images de profondeur. Une étude ergonomique nous permet de définir les indicateurs à mesurer. Ces indicateurs sont les zones d’évolution des mains de l’opérate
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Murali, Ram Subramanian. "Pose Estimation and 3D Reconstruction for 3D Dispensing." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288533.

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Currently in most of the cases the material deposition or dispensing is done only on planar surfaces, in applications such as extrusion 3D printing and surface mount technology (SMT) electronics assembly solutions. In future, the dispensing will be carried out on arbitrary three dimensional objects, where the dispenser needs to know the exact shape and location of them. This drives the necessity of using vision based high degree of freedom (DoF) robotic manipulator dispensers, instead of existing hard-coded Computer Numerical Control (CNC) based limited DoF 3D dispensers. Given a 3D object to
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Peñate, Sánchez Adrián. "3D pose estimation in complex environments." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/406085.

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Although there has been remarkable progress in the pose estimation literature, there are still a number of limitations when existing algorithms must be applied in everyday applications, especially in uncontrolled environments. This thesis has addressed some of these limitations, computing the pose for uncalibrated cameras, computing the pose without knowing the correspondence between 20 and 30 points, computing the pose when the points of interest are unreliable and computing the pose using only depth data. The problems addressed, and consequently their contributions, have been analyzed in or
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Pitteri, Giorgia. "3D Object Pose Estimation in Industrial Context." Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0202.

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La détection d'objets 3D et l'estimation de leur pose à partir d'images sont très importantes pour des tâches comme la robotique et la réalité augmentée et font l'objet d'intenses recherches depuis le début de la vision par ordinateur. D'importants progrès ont été réalisés récemment grâce au développement des méthodes basées sur l'apprentissage profond. Ce type d'approche fait néanmoins face à plusieurs obstacles majeurs qui se révèlent en milieu industriel, notamment la gestion des objets contenant des symétries et la généralisation à de nouveaux objets jamais vus par les réseaux lors de l'ap
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Madadi, Meysam. "Human segmentation, pose estimation and applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/457900.

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El análisis automático de seres humanos en fotografías o videos tiene grandes aplicaciones dentro de la visión por computador, incluyendo diagnóstico médico, deportes, entretenimiento, edición de películas y vigilancia, por nombrar sólo algunos. El cuerpo, la cara y la mano son los componentes más estudiados de los seres humanos. El cuerpo tiene muchas variabilidades en la forma y la ropa junto con altos grados de libertad en pose. La cara está compuesta por multitud de músculos, causando muchas deformaciones visibles, diferentes formas, y variabilidad en cabello. La mano es un objeto pequeño,
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Gkagkos, Polydefkis. "3D Human Pose and Shape-aware Modelling." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285922.

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The focus of this thesis is the task of 3D pose estimation while taking into consideration the shape of a person in a single image. For rendering the human pose and the body shape we use a newly proposed statistical model, the SMPL [1]. We train a neural network to estimate the shape and the pose of a person in an image. Afterwards, we use an optimization procedure to further enhance the output. the network is trained by incorporating the optimized and the predicted parameters into the loss. This approach is based on SPIN [2]. We extend this method by using a stronger optimization that is base
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Johnson, Samuel Alan. "Articulated human pose estimation in natural images." Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598026.

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In this thesis the problem of estimating the 2-D articulated pose, or configuration of a person in unconstrained images such as consumer photographs is addressed. Contributions are split among three major chapters. In previous work the Pictorial Structure Model approach has proven particularly successful. and is appealing because of its moderate computational cost. However, the accuracy of resulting pose estimates has been limited by the use of simple representations of limb appearance. In this thesis strong discriminatively trained limb detectors combining gradient and colour segmentation cue
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Oleinikov, Georgii. "Towards human pose estimation in video sequences." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/45767.

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Recent advancements in human pose estimation from single images have attracted wide scientific interest of the Computer Vision community to the problem domain. However, the problem of pose estimation from monocular video sequences is largely under-represented in the literature despite the wide range of its applications, such as action recognition and human-computer interaction. In this thesis we present two novel algorithms for video pose estimation that demonstrate how one could improve the performance of a state-of-the-art single-image articulated human detection algorithm on realistic video
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La, Gorce Martin de. "Model-based 3D hand pose estimation from monocular video." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2009. http://www.theses.fr/2009ECAP0045/document.

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Dans cette thèse sont présentées deux méthodes visant à obtenir automatiquement une description tridimensionnelle des mouvements d'une main étant donnée une séquence vidéo monoculaire de cette main. En utilisant l'information fournie par la vidéo, l'objectif est de déterminer l'ensemble des paramètres cinématiques nécessaires à la description de la configuration spatiale des différentes parties de la main. Cet ensemble de paramètres est composé des angles de chaque articulation ainsi que de la position et de l'orientation globale du poignet. Ce problème est un problème difficile. La main a de
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Cao, Hui, Yoshinori Takeuchi, Tetsuya Matsumoto, Hiroaki Kudo, and Noboru Ohnishi. "Recovering Human Pose by Collaborative Generative Models Estimation." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2005. http://hdl.handle.net/2237/10377.

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32

Zhu, Aichun. "Articulated human pose estimation in images and video." Thesis, Troyes, 2016. http://www.theses.fr/2016TROY0013/document.

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L’estimation de la pose du corps humain est un problème difficile en vision par ordinateur et les actions de toutes les difficultés de détection d’objet. Cette thèse se concentre sur les problèmes de l’estimation de la pose du corps humain dans les images ou vidéo, y compris la diversité des apparences, les changements de scène et l’éclairage de fond de confusion encombrement. Pour résoudre ces problèmes, nous construisons un modèle robuste comprenant les éléments suivants. Tout d’abord, les méthodes top-down et bottom-up sont combinés à l’estimation pose humaine. Nous étendons le modèle struc
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Navaratnam, Ramanan. "Probabilistic human body pose estimation from monocular images." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612174.

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Sandhu, Romeil Singh. "Statistical methods for 2D image segmentation and 3D pose estimation." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37245.

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The field of computer vision focuses on the goal of developing techniques to exploit and extract information from underlying data that may represent images or other multidimensional data. In particular, two well-studied problems in computer vision are the fundamental tasks of 2D image segmentation and 3D pose estimation from a 2D scene. In this thesis, we first introduce two novel methodologies that attempt to independently solve 2D image segmentation and 3D pose estimation separately. Then, by leveraging the advantages of certain techniques from each problem, we couple both tasks in a variat
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Thayananthan, Arasanathan. "Template-based pose estimation and tracking of 3D hand motion." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613782.

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36

Cetinkaya, Guven. "A Comparative Study On Pose Estimation Algorithms Using Visual Data." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614109/index.pdf.

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Computation of the position and orientation of an object with respect to a camera from its images is called pose estimation problem. Pose estimation is one of the major problems in computer vision, robotics and photogrammetry. Object tracking, object recognition, self-localization of robots are typical examples for the use of pose estimation. Determining the pose of an object from its projections requires 3D model of an object in its own reference system, the camera parameters and 2D image of the object. Most of the pose estimation algorithms require the correspondences between the 3D model po
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Brauer, Jürgen [Verfasser]. "Human Pose Estimation with Implicit Shape Models / Jürgen Brauer." Karlsruhe : KIT Scientific Publishing, 2014. http://www.ksp.kit.edu.

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Burke, Michael Glen. "Fast upper body pose estimation for human-robot interaction." Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/256305.

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This work describes an upper body pose tracker that finds a 3D pose estimate using video sequences obtained from a monocular camera, with applications in human-robot interaction in mind. A novel mixture of Ornstein-Uhlenbeck processes model, trained in a reduced dimensional subspace and designed for analytical tractability, is introduced. This model acts as a collection of mean-reverting random walks that pull towards more commonly observed poses. Pose tracking using this model can be Rao-Blackwellised, allowing for computational efficiency while still incorporating bio-mechanical properties o
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Zhu, Youding. "Model-Based Human Pose Estimation with Spatio-Temporal Inferencing." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1242752509.

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40

Krejov, Philip G. "Real time hand pose estimation for human computer interaction." Thesis, University of Surrey, 2016. http://epubs.surrey.ac.uk/809973/.

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The aim of this thesis is to address the challenge of real-time pose estimation of the hand. Specifically this thesis aims to determine the joint positions of a non-augmented hand. This thesis focuses on the use of depth, performing localisation of the parts of the hand for efficient fitting of a kinematic model and consists of four main contributions. The first contribution presents an approach to Multi-touch(less) tracking, where the objective is to track the fingertips with a high degree of accuracy without sensor contact. Using a graph based approach, the surface of the hand is modelled an
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41

Jaeggli, Tobias. "Statistical models for human body pose estimation from videos." Konstanz Hartung-Gorre, 2008. http://d-nb.info/991839315/04.

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Gomez-Donoso, Francisco. "Contributions to 3D object recognition and 3D hand pose estimation using deep learning techniques." Doctoral thesis, Universidad de Alicante, 2020. http://hdl.handle.net/10045/110658.

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In this thesis, a study of two blooming fields in the artificial intelligence topic is carried out. The first part of the present document is about 3D object recognition methods. Object recognition in general is about providing the ability to understand what objects appears in the input data of an intelligent system. Any robot, from industrial robots to social robots, could benefit of such capability to improve its performance and carry out high level tasks. In fact, this topic has been largely studied and some object recognition methods present in the state of the art outperform humans in ter
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43

Devanne, Maxime. "3D human behavior understanding by shape analysis of human motion and pose." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10138/document.

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L'émergence de capteurs de profondeur capturant la structure 3D de la scène et du corps humain offre de nouvelles possibilités pour l'étude du mouvement et la compréhension des comportements humains. Cependant, la conception et le développement de modules de reconnaissance de comportements à la fois précis et efficaces est une tâche difficile en raison de la variabilité de la posture humaine, la complexité du mouvement et les interactions avec l'environnement. Dans cette thèse, nous nous concentrons d'abord sur le problème de la reconnaissance d'actions en représentant la trajectoire du corps
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Cabras, Paolo. "3D Pose estimation of continuously deformable instruments in robotic endoscopic surgery." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD007/document.

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Connaître la position 3D d’instruments robotisés peut être très utile dans le contexte chirurgical. Nous proposons deux méthodes automatiques pour déduire la pose 3D d’un instrument avec une unique section pliable et équipé avec des marqueurs colorés, en utilisant uniquement les images fournies par la caméra monoculaire incorporée dans l'endoscope. Une méthode basée sur les graphes permet segmenter les marqueurs et leurs coins apparents sont extraits en détectant la transition de couleur le long des courbes de Bézier qui modélisent les points du bord. Ces primitives sont utilisées pour estimer
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Schick, Alexander [Verfasser], and R. [Akademischer Betreuer] Stiefelhagen. "Human Pose Estimation with Supervoxels / Alexander Schick. Betreuer: R. Stiefelhagen." Karlsruhe : KIT-Bibliothek, 2014. http://d-nb.info/1051371317/34.

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Dogan, Emre. "Human pose estimation and action recognition by multi-robot systems." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI060/document.

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L'estimation de la pose humaine et la reconnaissance des activités humaines sont des étapes importantes dans de nombreuses applications comme la robotique, la surveillance et la sécurité, etc. Actuellement abordées dans le domaine, ces tâches ne sont toujours pas résolues dans des environnements non-coopératifs particulièrement. Ces tâches admettent de divers défis comme l'occlusion, les variations des vêtements, etc. Les méthodes qui exploitent des images de profondeur ont l’avantage concernant les défis liés à l'arrière-plan et à l'apparence, pourtant, l’application est limitée pour des rais
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Lu, Yao. "Human body tracking and pose estimation from monocular image sequences." Thesis, Curtin University, 2013. http://hdl.handle.net/20.500.11937/1665.

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This thesis describes a bottom-up approach to estimating human pose over time based on monocular views with no restriction on human activities,Three approaches are proposed to address the weaknesses of existing approaches, including building a specific appearance model using clustering,utilising both the generic and specific appearance models in the estimation, and building an uncontaminated appearance model by removing backgroundpixels from the training samples. Experimental results show that the proposed system outperforms existing system significantly.
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Garau, Nicola. "Design of Viewpoint-Equivariant Networks to Improve Human Pose Estimation." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/345132.

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Human pose estimation (HPE) is an ever-growing research field, with an increasing number of publications in the computer vision and deep learning fields and it covers a multitude of practical scenarios, from sports to entertainment and from surveillance to medical applications. Despite the impressive results that can be obtained with HPE, there are still many problems that need to be tackled when dealing with real-world applications. Most of the issues are linked to a poor or completely wrong detection of the pose that emerges from the inability of the network to model the viewpoint. This thes
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Derkach, Dmytro. "Spectrum analysis methods for 3D facial expression recognition and head pose estimation." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/664578.

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Al llarg de les últimes dècades, l'anàlisi facial ha atret un interès creixent i considerable per part de la comunitat investigadora amb l’objectiu de millorar la interacció i la cooperació entre les persones i les màquines. Aquest interès ha propiciat la creació de sistemes automàtics capaços de reaccionar a diversos estímuls com ara els moviments del cap o les emocions d’una persona. Més enllà, les tasques automatitzades s’han de poder realitzar amb gran precisió dins d’entorns no controlats, fet que ressalta la necessitat d'algoritmes que aprofitin al màxim els avantatges que proporcionen l
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Lecrosnier, Louis. "Estimation de pose multimodale- Approche robuste par les droites 2D et 3D." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR089.

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Avec la complexification des tâches confiées aux robots, la perception de l’environnement, aussi appelée perception de scène, doit se faire de manière plus complète. L’emploi simultané de différents types de capteurs, ou multimodalité, est l’un des leviers employés à cet effet. L’exploitation de données issues de modalités différentes nécessite généralement de connaître la pose, c’est-à-dire l’orientation et la position, de chaque capteur relativement aux autres. Les travaux présentés dans cette thèse se concentrent sur la problématique d’estimation de pose robuste dans le cas d’une multimodal
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