Dissertationen zum Thema „Reconnaissance de Caméras“
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Ghorbel, Enjie. „Reconnaissance rapide et précise d'actions humaines à partir de caméras RGB-D“. Thesis, Normandie, 2017. http://www.theses.fr/2017NORMR027/document.
Der volle Inhalt der QuelleThe recent availability of RGB-D cameras has renewed the interest of researchers in the topic of human action recognition. More precisely, several action recognition methods have been proposed based on the novel modalities provided by these cameras, namely, depth maps and skeleton sequences. These approaches have been mainly evaluated in terms of recognition accuracy. This thesis aims to study the issue of fast action recognition from RGB-D cameras. It focuses on proposing an action recognition method realizing a trade-off between accuracy and latency for the purpose of applying it in real-time scenarios. As a first step, we propose a comparative study of recent RGB-D based action recognition methods using the two cited criteria: accuracy of recognition and rapidity of execution. Then, oriented by the conclusions stated thanks to this comparative study, we introduce a novel, fast and accurate human action descriptor called Kinematic Spline Curves (KSC).This latter is based on the cubic spline interpolation of kinematic values. Moreover, fast spatialand temporal normalization are proposed in order to overcome anthropometric variability, orientation variation and rate variability. The experiments carried out on four different benchmarks show the effectiveness of this approach in terms of execution time and accuracy. As a second step, another descriptor is introduced, called Hierarchical Kinematic Covariance(HKC). This latter is proposed in order to solve the issue of fast online action recognition. Since this descriptor does not belong to a Euclidean space, but is an element of the space of Symmetric Positive semi-definite (SPsD) matrices, we adapt kernel classification methods by the introduction of a novel distance called Modified Log-Euclidean, which is inspiredfrom Log-Euclidean distance. This extension allows us to use suitable classifiers to the feature space SPsD of matrices. The experiments prove the efficiency of our method, not only in terms of rapidity of calculation and accuracy, but also in terms of observational latency. These conclusions show that this approach combined with an action segmentation method could be appropriate to online recognition, and consequently, opens up new prospects for future works
Leroy, Vincent. „Modélisation 4D rapide et précise de larges séquences multi-caméras“. Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM042.
Der volle Inhalt der QuelleRecent advances in acquisition and processing technologies lead to the fast growth of a major branch in media production: volumetric video. In particular, the rise of virtual and augmented reality fuels an increased need for content suitable to these new media including 3D contents obtained from real scenes, as the ability to record a live performance and replay it from any given point of view allows the user to experience a realistic and immersive environment.This manuscript aims at presenting the problem of 4D shape reconstruction from multi-view RGB images, which is one way to create such content. We especially target real life performance capture, containing complex surface details. Typical challenges for these capture situations include smaller visual projection areas of objects of interest due to wider necessary fields of view for capturing motion; occlusion and self-occlusion of several subjects interacting together; lack of texture content typical of real-life subject appearance and clothing; or motion blur with fast moving subjects such as sport action scenes. An essential and still improvable aspect in this matter is the fidelity and quality of the recovered shapes, our goal in this work.We present a full reconstruction pipeline suited for this scenario, to which we contributed in many aspects. First, Multi-view stereo (MVS) based methods have attained a good level of quality with pipelines that typically comprise feature extraction, matching stages and 3D shape inference. Interestingly, very recent works have re-examined stereo and MVS by introducing features and similarity functions automatically inferred using deep learning, the main promise of this type of method being to include better data-driven priors. We examine in a first contribution whether these improvements transfer to the more general and complex case of live performance capture, where a diverse set of additional difficulties arise. We then explain how to use this learning strategy to robustly build a shape representation, from which can be extracted a 3D model. Once we obtain this representation at every frame of the captured sequence, we discuss how to exploit temporal redundancy for precision refinement by propagating shape details through adjacent frames. In addition to being beneficial to many dynamic multi-view scenarios this also enables larger scenes where such increased precision can compensate for the reduced spatial resolution per image frame. The source code implementing the different reconstruction methods is released to the public as open source software
Gond, Laétitia. „Système multi-caméras pour l'analyse de la posture humaine“. Clermont-Ferrand 2, 2009. http://www.theses.fr/2009CLF21922.
Der volle Inhalt der QuelleMousse, Ange Mikaël. „Reconnaissance d'activités humaines à partir de séquences multi-caméras : application à la détection de chute de personne“. Thesis, Littoral, 2016. http://www.theses.fr/2016DUNK0453/document.
Der volle Inhalt der QuelleArtificial vision is an involving field of research. The new strategies make it possible to have some autonomous networks of cameras. This leads to the development of many automatic surveillance applications using the cameras. The work developed in this thesis concerns the setting up of an intelligent video surveillance system for real-time people fall detection. The first part of our work consists of a robust estimation of the surface area of a person from two (02) cameras with complementary views. This estimation is based on the detection of each camera. In order to have a robust detection, we propose two approaches. The first approach consists in combining a motion detection algorithm based on the background modeling with an edge detection algorithm. A fusion approach has been proposed to make much more efficient the results of the detection. The second approach is based on the homogeneous regions of the image. A first segmentation is performed to find homogeneous regions of the image. And finally we model the background using obtained regions
Badie, Julien. „Optimisation du suivi de personnes dans un réseau de caméras“. Thesis, Nice, 2015. http://www.theses.fr/2015NICE4090/document.
Der volle Inhalt der QuelleThis thesis addresses the problem of improving the performance of people tracking process in a new framework called Global Tracker, which evaluates the quality of people trajectory (obtained by simple tracker) and recovers the potential errors from the previous stage. The first part of this Global Tracker estimates the quality of the tracking results, based on a statistical model analyzing the distribution of the target features to detect potential anomalies.To differentiate real errors from natural phenomena, we analyze all the interactions between the tracked object and its surroundings (other objects and background elements). In the second part, a post tracking method is designed to associate different tracklets (segments of trajectory) corresponding to the same person which were not associated by a first stage of tracking. This tracklet matching process selects the most relevant appearance features to compute a visual signature for each tracklet. Finally, the Global Tracker is evaluated with various benchmark datasets reproducing real-life situations, outperforming the state-of-the-art trackers
Letouzey, Antoine. „Modélisation 4D à partir de plusieurs caméras“. Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00771531.
Der volle Inhalt der QuelleFleuret, Laurence. „Unicité et ambiguïté de la reconstruction tridimensionnelle du mouvement de courbes rigides“. Nancy 1, 1998. http://www.theses.fr/1998NAN10241.
Der volle Inhalt der QuelleMeden, Boris. „Ré-identification de personnes : application aux réseaux de caméras à champs disjoints“. Phd thesis, Toulouse 3, 2013. http://thesesups.ups-tlse.fr/1952/.
Der volle Inhalt der QuelleThis thesis deals with intelligent videosurveillance, and focus on the supervision of camera networks with nonoverlapping fields of view, a classical constraint when it comes to limitate the building instrumentation. It is one of the use-case of the pedestrian re-identification problem. On that point, the thesis distinguishes itself from state of the art methods, which treat the problem from the descriptor perspective through image to image signatures comparison. Here we consider it from a bayesian filtering perspective : how to plug re-identification in a complete multi-target tracking process, in order to maintain targets identities, in spite of observation discontinuities. Thus we consider tracking and signature comparison, at the camera level, and use that module to take decisions at the network level. We describe first the classical re-identification approaches, based on the description. Then, we propose a mixed-state particle filter framework to estimate jointly the targets positions and their identities in the cameras. A second stage of processing integrates the network topology and optimise the re-identifications in the network. Considering the lack of public data in nonoverlapping camera network, we mainly demonstrate our approach on camera networks deployed at the lab. A publication of these data is in progress
Hamdoun, Omar. „Détection et ré-identification de piétons par points d'intérêt entre caméras disjointes“. Phd thesis, École Nationale Supérieure des Mines de Paris, 2010. http://pastel.archives-ouvertes.fr/pastel-00566417.
Der volle Inhalt der QuelleMeden, Boris. „Ré-identification de personnes : Application aux réseaux de caméras à champs disjoints“. Phd thesis, Université Paul Sabatier - Toulouse III, 2013. http://tel.archives-ouvertes.fr/tel-00822779.
Der volle Inhalt der QuelleMhiri, Rawia. „Approches 2D/2D pour le SFM à partir d'un réseau de caméras asynchrones“. Thesis, Rouen, INSA, 2015. http://www.theses.fr/2015ISAM0014/document.
Der volle Inhalt der QuelleDriver assistance systems and autonomous vehicles have reached a certain maturity in recent years through the use of advanced technologies. A fundamental step for these systems is the motion and the structure estimation (Structure From Motion) that accomplish several tasks, including the detection of obstacles and road marking, localisation and mapping. To estimate their movements, such systems use relatively expensive sensors. In order to market such systems on a large scale, it is necessary to develop applications with low cost devices. In this context, vision systems is a good alternative. A new method based on 2D/2D approaches from an asynchronous multi-camera network is presented to obtain the motion and the 3D structure at the absolute scale, focusing on estimating the scale factors. The proposed method, called Triangle Method, is based on the use of three images forming a. triangle shape: two images from the same camera and an image from a neighboring camera. The algorithrn has three assumptions: the cameras share common fields of view (two by two), the path between two consecutive images from a single camera is approximated by a line segment, and the cameras are calibrated. The extrinsic calibration between two cameras combined with the assumption of rectilinear motion of the system allows to estimate the absolute scale factors. The proposed method is accurate and robust for straight trajectories and present satisfactory results for curve trajectories. To refine the initial estimation, some en-ors due to the inaccuracies of the scale estimation are improved by an optimization method: a local bundle adjustment applied only on the absolute scale factors and the 3D points. The presented approach is validated on sequences of real road scenes, and evaluated with respect to the ground truth obtained through a differential GPS. Finally, another fundamental application in the fields of driver assistance and automated driving is road and obstacles detection. A method is presented for an asynchronous system based on sparse disparity maps
Benamara, Mohamed Adel. „Suivi visuel d'objets dans un réseau de caméras intelligentes : application au systèmes de manutention automatisés“. Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE2136.
Der volle Inhalt der QuelleIntralogistics (or internal logistics) focuses on the management and optimization of internal production and distribution processes within warehouses, distribution centers, and factories. Automated handling systems play a crucial role in the internal logistics of several industries such as e-commerce, postal messaging, retail, manufacturing, airport transport, etc. These systems are composed by multiple high-speed conveyor lines that provide safe and reliable transportation of a large volume of goods and merchandise while reducing costs.The automation of the conveying process relies on the identification and the real-time tracking of the transported loads. In this thesis, we designed a tracking solution that employs a network of smart cameras with an overlapping field of view. The goal is to provide tracking information to control an automated handling system.Multiple object tracking is a fundamental problem of computer vision that has many applications such as video surveillance, robotics, autonomous cars, etc. We integrated several building blocks traditionally applied to traffic surveillance or human activities monitoring to constitute a tracking pipeline. We used this baseline tracking pipeline to characterize contextual scene information proper to the conveying scenario. We integrated this contextual information to the tracking pipeline to enhance the performance. In particular, we took into account the state of moving objects that become stationary in the background subtraction step to prevent their absorption to the background model. We have also exploited the regularity of objects trajectory to enhance the motion model associated with the tracked objects. Finally, we integrated the precedence ordering constraint among the conveyed object to reidentify them when they are close to each other.We have also tackled practical problems related to the optimization the execution of the proposed tracking problem in the multi-core architectures of smart cameras. In particular, we proposed a dynamic learning process that extracts the region of the image that corresponds to the conveyor lines. We reduced the number of the processed pixel by restricting the processing to this region of interest. We also proposed a parallelization strategy that adaptively partitions this region of interest of the image, in order to balance the workload between the different cores of the smart cameras.Finally, we proposed a multiple cameras tracking algorithms based on event composition. This approach fuses the local tracking generated by the smart cameras to form global object trajectories and information from third party systems such as the destination of the object entered by operators on a terminal. We validated the proposed approach for the control of a sorting system deployed in a postal distribution warehouse. A network of cameras composed of 32 cameras tracks more than 400.000 parcel/day in injections lines. The tracking error rate is less than 1 parcel in a 1000 (0.1%)
Thériault, Olivier. „Intégration d'un système vidéo de poursuite de cible à un simulateur "hardware in the loop" d'avion sans pilote et évaluation d'algorithmes de surveillance“. Thesis, Université Laval, 2010. http://www.theses.ulaval.ca/2010/27137/27137.pdf.
Der volle Inhalt der QuelleZhang, Yiqun. „Contribution à l'étude de la vision dynamique : une approche basée sur la géométrie projective“. Compiègne, 1993. http://www.theses.fr/1993COMPD650.
Der volle Inhalt der QuelleHayet, Jean-Bernard. „Contribution à la navigation d'un robot mobile sur amers visuels texturés dans un environnement structuré“. Toulouse 3, 2003. http://www.theses.fr/2003TOU30026.
Der volle Inhalt der QuelleBerthet, Alexandre. „Deep learning methods and advancements in digital image forensics“. Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS252.
Der volle Inhalt der QuelleThe volume of digital visual data is increasing dramatically year after year. At the same time, image editing has become easier and more precise. Malicious modifications are therefore more accessible. Image forensics provides solutions to ensure the authenticity of digital visual data. Recognition of the source camera and detection of falsified images are among the main tasks. At first, the solutions were classical methods based on the artifacts produced during the creation of a digital image. Then, as in other areas of image processing, the methods moved to deep learning. First, we present a state-of-the-art survey of deep learning methods for image forensics. Our state-of-the-art survey highlights the need to apply pre-processing modules to extract artifacts hidden by image content. We also highlight the problems concerning image recognition evaluation protocols. Furthermore, we address counter-forensics and present compression based on artificial intelligence, which could be considered as an attack. In a second step, this thesis details three progressive evaluation protocols that address camera recognition problems. The final protocol, which is more reliable and reproducible, highlights the impossibility of state-of-the-art methods to recognize cameras in a challenging context. In a third step, we study the impact of compression based on artificial intelligence on two tasks analyzing compression artifacts: tamper detection and social network recognition. The performances obtained show on the one hand that this compression must be taken into account as an attack, but that it leads to a more important decrease than other manipulations for an equivalent image degradation
Zayed, Mohamed. „Véhicules intelligents : étude et développement d'un capteur intelligent de vision pour l'attelage virtuel“. Lille 1, 2005. https://ori-nuxeo.univ-lille1.fr/nuxeo/site/esupversions/b030da38-33c4-479d-b15b-10751fda9f2f.
Der volle Inhalt der QuelleLeyrit, Laetitia. „Reconnaissance d'objets en vision artificielle : application à la reconnaissance de piétons“. Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2010. http://tel.archives-ouvertes.fr/tel-00626492.
Der volle Inhalt der QuelleLavarec, Erwann. „Estimation de mouvements 3D à l'aide d'une caméra et de capteurs proprioceptifs“. Montpellier 2, 2001. http://www.theses.fr/2001MON20201.
Der volle Inhalt der QuelleMehmood, Muhammad Owais. „Détection de personnes pour des systèmes de videosurveillance multi-caméra intelligents“. Thesis, Ecole centrale de Lille, 2015. http://www.theses.fr/2015ECLI0016/document.
Der volle Inhalt der QuellePeople detection is a well-studied open challenge in the field of Computer Vision with applications such as in the visual surveillance systems. Monocular detectors have limited ability to handle occlusion, clutter, scale, density. Ubiquitous presence of cameras and computational resources fuel the development of multi-camera detection systems. In this thesis, we study the multi-camera people detection; specifically, the use of multi-view probabilistic occupancy maps based on the camera calibration. Occupancy maps allow multi-view geometric fusion of several camera views. Detection with such maps create several false detections and we study this phenomenon: ghost pruning. Further, we propose two novel techniques in order to improve multi-view detection based on: (a) kernel deconvolution, and (b) occupancy shape modeling. We perform non-temporal, multi-view reasoning in occupancy maps to recover accurate positions of people in challenging conditions such as of occlusion, clutter, lighting, and camera variations. We show improvements in people detections across three challenging datasets for visual surveillance including comparison with state-of-the-art techniques. We show the application of this work in exigent transportation scenarios i.e. people detection for surveillance at a train station and at an airport
Ghorpade, Vijaya Kumar. „3D Semantic SLAM of Indoor Environment with Single Depth Sensor“. Thesis, Université Clermont Auvergne (2017-2020), 2017. http://www.theses.fr/2017CLFAC085/document.
Der volle Inhalt der QuelleIntelligent autonomous actions in an ordinary environment by a mobile robot require maps. A map holds the spatial information about the environment and gives the 3D geometry of the surrounding of the robot to not only avoid collision with complex obstacles, but also selflocalization and for task planning. However, in the future, service and personal robots will prevail and need arises for the robot to interact with the environment in addition to localize and navigate. This interaction demands the next generation robots to understand, interpret its environment and perform tasks in human-centric form. A simple map of the environment is far from being sufficient for the robots to co-exist and assist humans in the future. Human beings effortlessly make map and interact with environment, and it is trivial task for them. However, for robots these frivolous tasks are complex conundrums. Layering the semantic information on regular geometric maps is the leap that helps an ordinary mobile robot to be a more intelligent autonomous system. A semantic map augments a general map with the information about entities, i.e., objects, functionalities, or events, that are located in the space. The inclusion of semantics in the map enhances the robot’s spatial knowledge representation and improves its performance in managing complex tasks and human interaction. Many approaches have been proposed to address the semantic SLAM problem with laser scanners and RGB-D time-of-flight sensors, but it is still in its nascent phase. In this thesis, an endeavour to solve semantic SLAM using one of the time-of-flight sensors which gives only depth information is proposed. Time-of-flight cameras have dramatically changed the field of range imaging, and surpassed the traditional scanners in terms of rapid acquisition of data, simplicity and price. And it is believed that these depth sensors will be ubiquitous in future robotic applications. In this thesis, an endeavour to solve semantic SLAM using one of the time-of-flight sensors which gives only depth information is proposed. Starting with a brief motivation in the first chapter for semantic stance in normal maps, the state-of-the-art methods are discussed in the second chapter. Before using the camera for data acquisition, the noise characteristics of it has been studied meticulously, and properly calibrated. The novel noise filtering algorithm developed in the process, helps to get clean data for better scan matching and SLAM. The quality of the SLAM process is evaluated using a context-based similarity score metric, which has been specifically designed for the type of acquisition parameters and the data which have been used. Abstracting semantic layer on the reconstructed point cloud from SLAM has been done in two stages. In large-scale higher-level semantic interpretation, the prominent surfaces in the indoor environment are extracted and recognized, they include surfaces like walls, door, ceiling, clutter. However, in indoor single scene object-level semantic interpretation, a single 2.5D scene from the camera is parsed and the objects, surfaces are recognized. The object recognition is achieved using a novel shape signature based on probability distribution of 3D keypoints that are most stable and repeatable. The classification of prominent surfaces and single scene semantic interpretation is done using supervised machine learning and deep learning systems. To this end, the object dataset and SLAM data are also made publicly available for academic research
Calvet, Lilian. „Méthodes de reconstruction tridimensionnelle intégrant des points cycliques : application au suivi d'une caméra“. Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2014. http://tel.archives-ouvertes.fr/tel-00981191.
Der volle Inhalt der QuelleChapel, Marie-Neige. „Détection d’objets en mouvement à l’aide d’une caméra mobile“. Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1156/document.
Der volle Inhalt der QuelleMoving objects detection in video streams is a commonly used technique in many computer vision algorithms. The detection becomes more complex when the camera is moving. The environment observed by this type of camera appeared moving and it is more difficult to distinguish the objects which are in movement from the others that composed the static part of the scene. In this thesis we propose contributions for the detection of moving objects in the video stream of a moving camera. The main idea to differenciate between moving and static objects based on 3D distances. 3D positions of feature points extracted from images are estimated by triangulation and then their 3D motions are analyzed in order to provide a sparse static/moving labeling. To provide a more robust detection, the analysis of the 3D motions is compared to those of feature points previously estimated static. A confidance value updated over time is used to decide on labels to attribute to each point.We make experiments on virtual (from the Previz project 1) and real datasets (known by the community [Och+14]) and we compare the results with the state of the art. The results show that our 3D constraint coupled with a statistical and temporal analysis of motions allow to detect moving elements in the video stream of a moving camera even in complex cases where apparent motions of the scene are not similars
Buat, Benjamin. „Caméra active 3D par Depth from Defocus pour l'inspection de surface : algorithmie, modèle de performance et réalisation expérimentale“. Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG058.
Der volle Inhalt der QuelleThis thesis is dedicated to the design of a 3D camera capable of producing the complete depth map of a scene within the framework of surface inspection. This field of application generally involves objects with little texture and strict specifications concerning the compactness of the inspection system and the precision required. In this thesis, we propose to use a camera combined with a projector to add an artificial texture to the scene. 3D extraction is based on the principle of “Depth-From-Defocus” which consists in estimating the depth by exploiting the defocus blur. We first developed a single-image local depth estimation algorithm based on scene and blur learning. This algorithm works for any type of DFD system but it is particularly suitable for active DFD for which we control the scene which is a projected texture. Then we implemented an experimental active DFD prototype for surface inspection. It is composed of a chromatic camera whose lens has longitudinal chromatic aberrations to extend the estimable depth range and estimation accuracy, and a specialized projector whose pattern shape and scale have been particularly optimized by the simulation of the prototype. We also carried out an experimental an experimental validation of the prototype which achieved an accuracy of 0.45 mm over a working range of 310 to 340 mm. We then developed a performance model that predicts the accuracy of any active DFD system depending on the parameters of the optics, sensor, projector and treatments. This model paves the way for a joint optical/processing design study of an active 3D camera by DFD
Michoud, Brice. „Reconstruction 3D à partir de séquences vidéo pour l’acquisition du mouvement de personnages en temps réel et sans marqueur“. Thesis, Lyon 1, 2009. http://www.theses.fr/2009LYO10156/document.
Der volle Inhalt der QuelleWe aim at automatically capturing 3D motion of persons without markers. To make it flexible, and to consider interactive applications, we address real-time solution, without specialized instrumentation. Real-time body estimation and shape analyze lead to home motion capture application. We begin by addressing the problem of 3D real-time reconstruction of moving objects from multiple views. Existing approaches often involve complex computation methods, making them incompatible with real-time constraints. Shape-From-Silhouette (SFS) approaches provide interesting compromise between algorithm efficiency and accuracy. They estimate 3D objects from their silhouettes in each camera. However they require constrained environments and cameras placement. The works presented in this document generalize the use of SFS approaches to uncontrolled environments. The main methods of marker-less motion capture, are based on parametric modeling of the human body. The acquisition of movement goal is to determine the parameters that provide the best correlation between the model and the 3D reconstruction.The following approaches, more robust, use natural markings of the body extremities: the skin. Coupled with a temporal Kalman filter, a registration of simple geometric objects, or an ellipsoids' decomposition, we have proposed two real-time approaches, providing a mean error of 6%. Thanks to the approach robustness, it allows the simultaneous monitoring of several people even in contacts. The results obtained open up prospects for a transfer to home applications
Souded, Malik. „Détection, suivi et ré-identification de personnes à travers un réseau de caméra vidéo“. Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00913072.
Der volle Inhalt der QuelleAtohoun, Béthel Christian A. R. K. „Architecture logique d'un système multi agents de suivi multi caméra distribué : exploitation du modèle de croyance transférable“. Thesis, Littoral, 2013. http://www.theses.fr/2013DUNK0373/document.
Der volle Inhalt der QuelleThis thesis presents the joint use of the theory of evidence and multiple hypothesis tracking for modeling and managing a system for monitoring multiple cameras in a motorway. The tracking is based on the re-identification of objects (vehicles) on the basis of visuals and times informations. A realization of these concepts results in the design and implementation of a software architecture for multiple agents management of multiple camera tracking system. After presenting the state of the art on the frameworks of uncertainty management and that on information fusion for the matching, and the multi-agent systems, our contribution in this work is on two or three levels. The first was an adaptation of the decision phase of the transferable belief model to incorporate the use of multi-hypotheses tracking as a tool of ambiguity survey in case of indecision in matching situation. The second contribution was a proposition of agent-based software architecture for management of a multiple cameras tracking system. We have proposed the global system modeling as well as agents and their interactions modeling using a personal analysis method but nevertheless inspired by modelisation languages and tolls such as Agent UML, MaSE and others, because there is not yet a standard and normalized tool on the subject. Our third contribution was to begin an implementation of our agent-based software architecture using JADE (Java Agent Development Framework). Some experiment and discussions are presented at the end to lead to our conclusions and perspectives
Wozniak, Peter. „Range imaging based obstacle detection for virtual environment systems and interactive metaphor based signalization“. Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAD013/document.
Der volle Inhalt der QuelleWith this generation of devices, virtual reality (VR) has actually made it into the living rooms of end-users. These devices feature 6 degrees of freedom tracking, allowing them to move naturally in virtual worlds. However, for a natural locomotion in the virtual, one needs a corresponding free space in the real environment. The available space is often limited. Objects of daily life can quickly become obstacles for VR users if they are not cleared away. The currently available systems offer only rudimentary assistance for this problem. There is no detection of potentially dangerous objects. This thesis shows how obstacles can be detected automatically with range imaging cameras and how users can be effectively warned about them in the virtual environment. 4 visual metaphors were evaluated with the help of a user study
Boui, Marouane. „Détection et suivi de personnes par vision omnidirectionnelle : approche 2D et 3D“. Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLE009/document.
Der volle Inhalt der QuelleIn this thesis we will handle the problem of 3D people detection and tracking in omnidirectional images sequences, in order to realize applications allowing3D pose estimation, we investigate the problem of 3D people detection and tracking in omnidirectional images sequences. This requires a stable and accurate monitoring of the person in a real environment. In order to achieve this, we will use a catadioptric camera composed of a spherical mirror and a perspective camera. This type of sensor is commonly used in computer vision and robotics. Its main advantage is its wide field of vision, which allows it to acquire a 360-degree view of the scene with a single sensor and in a single image. However, this kind of sensor generally generates significant distortions in the images, not allowing a direct application of the methods conventionally used in perspective vision. Our thesis contains a description of two monitoring approaches that take into account these distortions. These methods show the progress of our work during these three years, allowing us to move from person detection to the 3Destimation of its pose. The first step of this work consisted in setting up a person detection algorithm in the omnidirectional images. We proposed to extend the conventional approach for human detection in perspective image, based on the Gradient-Oriented Histogram (HOG), in order to adjust it to spherical images. Our approach uses the Riemannian varieties to adapt the gradient calculation for omnidirectional images as well as the spherical gradient for spherical images to generate our omnidirectional image descriptor
Li, You. „Stereo vision and LIDAR based Dynamic Occupancy Grid mapping : Application to scenes analysis for Intelligent Vehicles“. Phd thesis, Université de Technologie de Belfort-Montbeliard, 2013. http://tel.archives-ouvertes.fr/tel-00982325.
Der volle Inhalt der QuelleDubois, Amandine. „Mesure de la fragilité et détection de chutes pour le maintien à domicile des personnes âgées“. Phd thesis, Université de Lorraine, 2014. http://tel.archives-ouvertes.fr/tel-01070972.
Der volle Inhalt der QuelleFofi, David. „Contributions à la Vision par Ordinateur pour les Systèmes en Lumière Structurée et les Systèmes Catadioptriques“. Habilitation à diriger des recherches, Université de Bourgogne, 2008. http://tel.archives-ouvertes.fr/tel-00950264.
Der volle Inhalt der QuelleCorsino, Espino Jorge. „Détection de rails, caractérisation de croisements et localisation de trains sur la trajectoire d'un métro automatique“. Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2014. http://pastel.archives-ouvertes.fr/pastel-01068899.
Der volle Inhalt der QuelleDang, Quoc Bao. „Information spotting in huge repositories of scanned document images“. Thesis, La Rochelle, 2018. http://www.theses.fr/2018LAROS024/document.
Der volle Inhalt der QuelleThis work aims at developing a generic framework which is able to produce camera-based applications of information spotting in huge repositories of heterogeneous content document images via local descriptors. The targeted systems may take as input a portion of an image acquired as a query and the system is capable of returning focused portion of database image that match the query best. We firstly propose a set of generic feature descriptors for camera-based document images retrieval and spotting systems. Our proposed descriptors comprise SRIF, PSRIF, DELTRIF and SSKSRIF that are built from spatial space information of nearest keypoints around a keypoints which are extracted from centroids of connected components. From these keypoints, the invariant geometrical features are considered to be taken into account for the descriptor. SRIF and PSRIF are computed from a local set of m nearest keypoints around a keypoint. While DELTRIF and SSKSRIF can fix the way to combine local shape description without using parameter via Delaunay triangulation formed from a set of keypoints extracted from a document image. Furthermore, we propose a framework to compute the descriptors based on spatial space of dedicated keypoints e.g SURF or SIFT or ORB so that they can deal with heterogeneous-content camera-based document image retrieval and spotting. In practice, a large-scale indexing system with an enormous of descriptors put the burdens for memory when they are stored. In addition, high dimension of descriptors can make the accuracy of indexing reduce. We propose three robust indexing frameworks that can be employed without storing local descriptors in the memory for saving memory and speeding up retrieval time by discarding distance validating. The randomized clustering tree indexing inherits kd-tree, kmean-tree and random forest from the way to select K dimensions randomly combined with the highest variance dimension from each node of the tree. We also proposed the weighted Euclidean distance between two data points that is computed and oriented the highest variance dimension. The secondly proposed hashing relies on an indexing system that employs one simple hash table for indexing and retrieving without storing database descriptors. Besides, we propose an extended hashing based method for indexing multi-kinds of features coming from multi-layer of the image. Along with proposed descriptors as well indexing frameworks, we proposed a simple robust way to compute shape orientation of MSER regions so that they can combine with dedicated descriptors (e.g SIFT, SURF, ORB and etc.) rotation invariantly. In the case that descriptors are able to capture neighborhood information around MSER regions, we propose a way to extend MSER regions by increasing the radius of each region. This strategy can be also applied for other detected regions in order to make descriptors be more distinctive. Moreover, we employed the extended hashing based method for indexing multi-kinds of features from multi-layer of images. This system are not only applied for uniform feature type but also multiple feature types from multi-layers separated. Finally, in order to assess the performances of our contributions, and based on the assessment that no public dataset exists for camera-based document image retrieval and spotting systems, we built a new dataset which has been made freely and publicly available for the scientific community. This dataset contains portions of document images acquired via a camera as a query. It is composed of three kinds of information: textual content, graphical content and heterogeneous content
De, goussencourt Timothée. „Système multimodal de prévisualisation “on set” pour le cinéma“. Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT106/document.
Der volle Inhalt der QuellePreviz on-set is a preview step that takes place directly during the shootingphase of a film with special effects. The aim of previz on-set is to show to the film director anassembled view of the final plan in realtime. The work presented in this thesis focuses on aspecific step of the previz : the compositing. This step consists in mixing multiple images tocompose a single and coherent one. In our case, it is to mix computer graphics with an imagefrom the main camera. The objective of this thesis is to propose a system for automaticadjustment of the compositing. The method requires the measurement of the geometry ofthe scene filmed. For this reason, a depth sensor is added to the main camera. The data issent to the computer that executes an algorithm to merge data from depth sensor and themain camera. Through a hardware demonstrator, we formalized an integrated solution in avideo game engine. The experiments gives encouraging results for compositing in real time.Improved results were observed with the introduction of a joint segmentation method usingdepth and color information. The main strength of this work lies in the development of ademonstrator that allowed us to obtain effective algorithms in the field of previz on-set
Sun, Haixin. „Moving Objects Detection and Tracking using Hybrid Event-based and Frame-based Vision for Autonomous Driving“. Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0014.
Der volle Inhalt der QuelleThe event-based camera is a bioinspiredsensor that differs from conventionalframe cameras: Instead of grabbing frameimages at a fixed rate, they asynchronouslymonitor per-pixel brightness change and outputa stream of events data that contains the time,location and sign of the brightness changes.Event cameras offer attractive propertiescompared to traditional cameras: high temporalresolution, high dynamic range, and low powerconsumption. Therefore, event cameras have anenormous potential for computer vision inchallenging scenarios for traditional framecameras, such as fast motion, and high dynamicrange.This thesis investigated the model-based anddeep-learning-based for object detection andtracking with the event camera. The fusionstrategy with the frame camera is proposedsince the frame camera is also needed toprovides appearance infomation. The proposedperception algorithms include optical flow,object detection and motion segmentation.Tests and analyses have been conducted toprove the feasibility and reliability of theproposed perception algorithms
Laviole, Jérémy. „Interaction en réalité augmentée spatiale pour le dessin physique“. Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00935602.
Der volle Inhalt der QuelleCapellier, Édouard. „Application of machine learning techniques for evidential 3D perception, in the context of autonomous driving“. Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2534.
Der volle Inhalt der QuelleThe perception task is paramount for self-driving vehicles. Being able to extract accurate and significant information from sensor inputs is mandatory, so as to ensure a safe operation. The recent progresses of machine-learning techniques revolutionize the way perception modules, for autonomous driving, are being developed and evaluated, while allowing to vastly overpass previous state-of-the-art results in practically all the perception-related tasks. Therefore, efficient and accurate ways to model the knowledge that is used by a self-driving vehicle is mandatory. Indeed, self-awareness, and appropriate modeling of the doubts, are desirable properties for such system. In this work, we assumed that the evidence theory was an efficient way to finely model the information extracted from deep neural networks. Based on those intuitions, we developed three perception modules that rely on machine learning, and the evidence theory. Those modules were tested on real-life data. First, we proposed an asynchronous evidential occupancy grid mapping algorithm, that fused semantic segmentation results obtained from RGB images, and LIDAR scans. Its asynchronous nature makes it particularly efficient to handle sensor failures. The semantic information is used to define decay rates at the cell level, and handle potentially moving object. Then, we proposed an evidential classifier of LIDAR objects. This system is trained to distinguish between vehicles and vulnerable road users, that are detected via a clustering algorithm. The classifier can be reinterpreted as performing a fusion of simple evidential mass functions. Moreover, a simple statistical filtering scheme can be used to filter outputs of the classifier that are incoherent with regards to the training set, so as to allow the classifier to work in open world, and reject other types of objects. Finally, we investigated the possibility to perform road detection in LIDAR scans, from deep neural networks. We proposed two architectures that are inspired by recent state-of-the-art LIDAR processing systems. A training dataset was acquired and labeled in a semi-automatic fashion from road maps. A set of fused neural networks reaches satisfactory results, which allowed us to use them in an evidential road mapping and object detection algorithm, that manages to run at 10 Hz
Zhou, Shuting. „Navigation of a quad-rotor to access the interior of a building“. Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2237.
Der volle Inhalt der QuelleThis research work is dedicated to the development of an autonomous navigation strategy which includes generating an optimal trajectory with obstacles avoiding capabilities, detecting specific object of interest (i.e. a window) and then conducting the subsequent maneuver to approach the window and finally access into the building. The vehicle is navigated by a vision system and a combination of inertial and altitude sensors, which achieve a relative localization of the quad-rotor with respect to its surrounding environment. A MPC-based path planning method using the information provided by the GPS and the visual sensor has been developed to generate an optimal real-time trajectory with collision avoidance capabilities, which starts from an initial point given by the user and guides the vehicle to achieve the final point outside the target building. With the aim of detecting and locating the object of interest, two different vision-based object detection strategies are proposed and are applied respectively in the stereo vision system and the vision system using the Kinect. After estimating the target window model, a motion estimation framework is developed to estimate the vehicle’s ego-motion from the images provided by the visual sensor. There have been two versions of the motion estimation frameworks for both vision systems. A quad-rotor experimental platform is developed. For estimating the translational dynamic of the vehicle, a Kalman filter is implemented to combine the imaging, inertial and altitude sensors. A hierarchical sensing and control system is designed to perform the navigation and control of the quad-rotor helicopter, which allows the vehicle to estimate the state without artificial marks or other external positioning systems
Gouiaa, Rafik. „Reconnaissance de postures humaines par fusion de la silhouette et de l'ombre dans l'infrarouge“. Thèse, 2017. http://hdl.handle.net/1866/19538.
Der volle Inhalt der QuelleHuman posture recognition (HPR) from video sequences is one of the major active research areas of computer vision. It is one step of the global process of human activity recognition (HAR) for behaviors analysis. Many HPR application systems have been developed including video surveillance, human-machine interaction, and the video retrieval. Generally, applications related to HPR can be achieved using mainly two approaches : single camera or multi-cameras. Despite the interesting performance achieved by multi-camera systems, their complexity and the huge information to be processed greatly limit their widespread use for HPR. The main goal of this thesis is to simplify the multi-camera system by replacing a camera by a light source. In fact, a light source can be seen as a virtual camera, which generates a cast shadow image representing the silhouette of the person that blocks the light. Our system will consist of a single camera and one or more infrared light sources. Despite some technical difficulties in cast shadow segmentation and cast shadow deformation because of walls and furniture, different advantages can be achieved by using our system. Indeed, we can avoid the synchronization and calibration problems of multiple cameras, reducing the cost of the system and the amount of processed data by replacing a camera by one light source. We introduce two different approaches in order to automatically recognize human postures. The first approach directly combines the person’s silhouette and cast shadow information, and uses 2D silhouette descriptor in order to extract discriminative features useful for HPR. The second approach is inspired from the shape from silhouette technique to reconstruct the visual hull of the posture using a set of cast shadow silhouettes, and extract informative features through 3D shape descriptor. Using these approaches, our goal is to prove the utility of the combination of person’s silhouette and cast shadow information for recognizing elementary human postures (stand, bend, crouch, fall,...) The proposed system can be used for video surveillance of uncluttered areas such as a corridor in a senior’s residence (for example, for the detection of falls) or in a company (for security). Its low cost may allow greater use of video surveillance for the benefit of society.