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Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior / Maximilian Alexander Coenen in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 27 - 47 Titre : Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior Type de document : Article/Communication Auteurs : Maximilian Alexander Coenen, Auteur ; Franz Rottensteiner, Auteur Année de publication : 2021 Article en page(s) : pp 27 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] estimation de pose [Termes IGN] modèle stochastique [Termes IGN] problème inverse [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] robotique [Termes IGN] véhicule automobile [Termes IGN] vision par ordinateur Résumé : (auteur) The 3D reconstruction of objects is a prerequisite for many highly relevant applications of computer vision such as mobile robotics or autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstruction approach by establishing a 3D model and aligning it to the 2D image plane. However, current approaches are limited due to inadequate shape priors and the insufficiency of the derived image observations for a reliable alignment with the 3D model. The goal of this paper is to show how 3D object reconstruction can profit from a more sophisticated shape prior and from a combined incorporation of different observation types inferred from the images. We introduce a subcategory-aware deformable vehicle model that makes use of a prediction of the vehicle type for a more appropriate regularisation of the vehicle shape. A multi-branch CNN is presented to derive predictions of the vehicle type and orientation. This information is also introduced as prior information for model fitting. Furthermore, the CNN extracts vehicle keypoints and wireframes, which are well-suited for model-to-image association and model fitting. The task of pose estimation and reconstruction is addressed by a versatile probabilistic model. Extensive experiments are conducted using two challenging real-world data sets on both of which the benefit of the developed shape prior can be shown. A comparison to state-of-the-art methods for vehicle pose estimation shows that the proposed approach performs on par or better, confirming the suitability of the developed shape prior and probabilistic model for vehicle reconstruction. Numéro de notice : A2021-772 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.006 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98829 [article]

The point-descriptor-precedence representation for point configurations and movements / Amna Qayyum in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1374 - 1391 Titre : The point-descriptor-precedence representation for point configurations and movements Type de document : Article/Communication Auteurs : Amna Qayyum, Auteur ; Bernard De Baets, Auteur ; Muhammad Sulman Baig, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1374 - 1391 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] courbe [Termes IGN] détection d'événement [Termes IGN] données spatiotemporelles [Termes IGN] mesurage de distances [Termes IGN] objet mobile [Termes IGN] reconnaissance de formes [Termes IGN] relation topologique [Termes IGN] trafic routier [Termes IGN] véhicule automobile Résumé : (auteur) In this paper, we represent (moving) point configurations along a curved directed line qualitatively by means of a system of relational symbols based on two distance descriptors: one representing distance along the curved directed line and the other representing signed orthogonal distance to the curved directed line. The curved directed line represents the direction of the movement of interest. For instance, it could be straight as in the case of driving along a highway or could be curved as in the case of an intersection or a roundabout. Inspired by the Point Calculus, the order between the points on the curved directed line is described by means of a small set of binary relations () acting upon the distance descriptors. We call this representation the Point-Descriptor-Precedence-Static (PDPS) representation at a time point and Point-Descriptor-Precedence-Dynamic (PDPD) representation during a time interval. To illustrate how the proposed approach can be used to represent and analyse curved movements, some basic micro-analysis traffic examples are studied. Finally, we discuss some extensions of our work to highlight the practical benefits of PDP in identifying motion patterns that could be useful in GIS, autonomous vehicles, sports analytics, and gait analysis. Numéro de notice : A2021-453 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1864378 Date de publication en ligne : 11/01/2021 En ligne : https://doi.org/10.1080/13658816.2020.1864378 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97882 [article]

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An introduction to ethics in robotics and AI / Christoph Bartneck (2021)  

Public Titre : An introduction to ethics in robotics and AI Type de document : Monographie Auteurs : Christoph Bartneck, Auteur ; Christoph Lütge, Auteur ; Alan Wagner, Auteur ; Sean Welsch, Auteur Editeur : Springer International Publishing Année de publication : 2021 Importance : 117 p Présentation : . Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-51110-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] données massives [Termes IGN] édition en libre accès [Termes IGN] éthique [Termes IGN] navigation autonome [Termes IGN] protection de la vie privée [Termes IGN] robotique [Termes IGN] utilisateur militaire [Termes IGN] véhicule sans pilote Résumé : (éditeur) This open access book introduces the reader to the foundations of AI and ethics. It discusses issues of trust, responsibility, liability, privacy and risk. It focuses on the interaction between people and the AI systems and Robotics they use. Designed to be accessible for a broad audience, reading this book does not require prerequisite technical, legal or philosophical expertise. Throughout, the authors use examples to illustrate the issues at hand and conclude the book with a discussion on the application areas of AI and Robotics, in particular autonomous vehicles, automatic weapon systems and biased algorithms. A list of questions and further readings is also included for students willing to explore the topic further. Note de contenu : 1- What is AI? 2- What is ethics? 3- Trust and fairness in AI systems 4- Responsibility and liability in the case of AI systems 5- Risks in the business of AI 6- Psychological aspects of AI 7- Privacy issues of AI 8- Application areas of AI 9- Autonomous vehicles 10- Military uses of AI 11- Ethics in AI and Robotics: A Strategic Challenge Numéro de notice : 28570 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-51110-4 En ligne : https://doi.org/10.1007/978-3-030-51110-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97726

Real-time multimodal semantic scene understanding for autonomous UGV navigation / Yifei Zhang (2021)  

Public Titre : Real-time multimodal semantic scene understanding for autonomous UGV navigation Type de document : Thèse/HDR Auteurs : Yifei Zhang, Auteur ; Fabrice Mériaudeau, Directeur de thèse ; Désiré Sidibé, Directeur de thèse Editeur : Dijon : Université Bourgogne Franche-Comté UBFC Année de publication : 2021 Importance : 114 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse pour obtenir le doctorat de l'Université Bourgogne Franche-Comté, Spécialité Instrumentation et informatique d’image Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] données polarimétriques [Termes IGN] fusion d'images [Termes IGN] image RVB [Termes IGN] intégration de données [Termes IGN] navigation autonome [Termes IGN] segmentation sémantique [Termes IGN] temps réel [Termes IGN] véhicule sans pilote Résumé : (Auteur) Robust semantic scene understanding is challenging due to complex object types, as well as environmental changes caused by varying illumination and weather conditions. This thesis studies the problem of deep semantic segmentation with multimodal image inputs. Multimodal images captured from various sensory modalities provide complementary information for complete scene understanding. We provided effective solutions for fully-supervised multimodal image segmentation and few-shot semantic segmentation of the outdoor road scene. Regarding the former case, we proposed a multi-level fusion network to integrate RGB and polarimetric images. A central fusion framework was also introduced to adaptively learn the joint representations of modality-specific features and reduce model uncertainty via statistical post-processing.In the case of semi-supervised semantic scene understanding, we first proposed a novel few-shot segmentation method based on the prototypical network, which employs multiscale feature enhancement and the attention mechanism. Then we extended the RGB-centric algorithms to take advantage of supplementary depth cues. Comprehensive empirical evaluations on different benchmark datasets demonstrate that all the proposed algorithms achieve superior performance in terms of accuracy as well as demonstrating the effectiveness of complementary modalities for outdoor scene understanding for autonomous navigation. Note de contenu : 1. Introduction 1.1 Context and Motivation 1.2 Background and Challenges 1.3 Contributions 1.4 Organization 2. Background on Neural Networks 2.1 Basic Concepts 2.2 Neural Network Layers 2.3 Optimization 2.4 Model Training 2.5 Evaluation Metrics 2.6 Summary 3. Literature Review 3.1 Fully-supervised Semantic Image 3.2 Datasets 3.3 Summary 4. Deep Multimodal Fusion for Semantic Image Segmentation 4.1 CMNet: Deep Multimodal Fusion 4.2 A Central Multimodal Fusion Framework 4.3 Summary 5. Few-shot Semantic Image Segmentation 5.1 Introduction on Few-shot Segmentation 5.2 MAPnet: A Multiscale Attention-Based Prototypical Network 5.3 RDNet: Incorporating Depth Information into Few-shot Segmentation 5.4 Summary 6. Conclusion and Future Work 6.1 General Conclusion 6.2 Future Perspectives Numéro de notice : 26527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Instrumentation et informatique d’image : Bourgogne : 2021 nature-HAL : Thèse Date de publication en ligne : 02/03/2021 En ligne : https://tel.archives-ouvertes.fr/tel-03154783/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97556

A history of laser scanning, Part 1: space and defense applications / Adam P. Spring in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 7 (July 2020) . - pp 419-429 Titre : A history of laser scanning, Part 1: space and defense applications Type de document : Article/Communication Auteurs : Adam P. Spring, Auteur Année de publication : 2020 Article en page(s) : pp 419-429 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] balayage laser [Termes IGN] capteur à balayage [Termes IGN] défense nationale [Termes IGN] histoire des sciences et techniques [Termes IGN] navigation autonome [Termes IGN] secteur spatial [Termes IGN] semis de points [Termes IGN] véhicule sans pilote Résumé : (Auteur) This article presents the origins and evolution of midrange terrestrial laser scanning (TLS), spanning primarily from the 1950s to the time of publication. Particular attention is given to developments in hardware and software that document the physical dimensions of a scene as a point cloud. These developments include parameters for accuracy, repeatability, and resolution in the midrange—millimeter and centimeter levels when recording objects at building and landscape scales up to a kilometer away. The article is split into two parts: Part one starts with early space and defense applications, and part two examines the survey applications that formed around TLS technologies in the 1990s. The origins of midrange TLS, ironically, begin in space and defense applications, which shaped the development of sensors and information processing via autonomous vehicles. Included are planetary rovers, space shuttles, robots, and land vehicles designed for relative navigation in hostile environments like space and war zones. Key people in the midrange TLS community were consulted throughout the 10-year period over which this article was written. A multilingual and multidisciplinary literature review—comprising media written or produced in Chinese, English, French, German, Japanese, Italian, and Russian—was also an integral part of this research. Numéro de notice : A2020-381 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.7.419 Date de publication en ligne : 01/07/2020 En ligne : https://doi.org/10.14358/PERS.86.7.419 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95426 [article]

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A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery / Mehdi Khoshboresh Masouleh in Applied geomatics, vol 12 n° 2 (June 2020)  

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A method for urban population density prediction at 30m resolution / Krishnachandran Balakrishnan in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

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Suitable location selection for the electric vehicle fast charging station with AHP and fuzzy AHP methods using GIS / Dogus Guler in Annals of GIS, vol 26 n° 2 (April 2020)  

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Probabilistic pose estimation and 3D reconstruction of vehicles from stereo images / Maximilian Alexander Coenen (2020)  

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Scene context-driven vehicle detection in high-resolution aerial images / Chao Tao in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

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Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

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Vehicle detection in aerial images / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 4 (avril 2019)  

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Rapport d'activité 2018 de l'Institut National de l'Information Géographique et Forestière IGN, 2. Panorama 2018 / Institut national de l'information géographique et forestière (2012 -) (2019)

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Towards visual urban scene understanding for autonomous vehicle path tracking using GPS positioning data / Citlalli Gamez Serna (2019)  

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De la navigation connectée à la voiture autonome - partie 2 Et mon tout est un véhicule autonome / Hubert d' Erceville in SIGmag, n° 17 (juin 2018)

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