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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]

Dynamic human body reconstruction and motion tracking with low-cost depth cameras / Kangkan Wang in The Visual Computer, vol 37 n° 3 (March 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 3 (March 2021) . - pp 603 - 618 Titre : Dynamic human body reconstruction and motion tracking with low-cost depth cameras Type de document : Article/Communication Auteurs : Kangkan Wang, Auteur ; Guofeng Zhang, Auteur ; Jian Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 603 - 618 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement de formes [Termes IGN] déformation de projection [Termes IGN] données d'apprentissage [Termes IGN] filtrage spatiotemporel [Termes IGN] maillage [Termes IGN] modèle dynamique [Termes IGN] modélisation 3D [Termes IGN] mouvement [Termes IGN] objet mobile [Termes IGN] reconstruction d'objet [Termes IGN] squelettisation Résumé : (auteur) We present a novel approach for dynamic human body reconstruction and motion tracking using low-cost depth cameras. Our reconstruction system is able to produce a sequence of dynamic 3D human body models from the noisy input depth data. To accurately align the template model with noisy input data, we combine skeleton-driven deformation and mesh deformation techniques to enhance the registration robustness to depth missing, occlusions, and severe noise. In addition, a novel data-driven 3D human body model is introduced to efficiently reconstruct human body models with wide shape and pose variations only using a limited number of training databases with standard standing pose. We perform quantitative and qualitative experiments to evaluate our method and compare it with other methods for body reconstruction on both synthetic and real datasets. Experimental results demonstrate the effectiveness of the proposed approach. Numéro de notice : A2021-341 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01826-4 date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1007/s00371-020-01826-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97579 [article]

Activity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)  

Public [article]  inSensors > vol 21 n° 3 (February 2021) . - n° 988 Titre : Activity recognition in residential spaces with Internet of things devices and thermal imaging Type de document : Article/Communication Auteurs : Kshirasagar Naik, Auteur ; Tejas Pandit, Auteur ; Nitin Naik, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 988 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] compréhension de l'image [Termes IGN] contrôle par télédétection [Termes IGN] détection d'événement [Termes IGN] espace intérieur [Termes IGN] image RVB [Termes IGN] image thermique [Termes IGN] intelligence artificielle [Termes IGN] internet des objets [Termes IGN] itération [Termes IGN] modèle stéréoscopique [Termes IGN] objet mobile [Termes IGN] reconnaissance automatique [Termes IGN] reconnaissance d'objets [Termes IGN] scène 3D Résumé : (auteur) In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces. Numéro de notice : A2021-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s21030988 date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.3390/s21030988 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97075 [article]

Unsupervised deep representation learning for real-time tracking / Ning Wang in International journal of computer vision, vol 129 n° 2 (February 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 2 (February 2021) . - pp 400 - 418 Titre : Unsupervised deep representation learning for real-time tracking Type de document : Article/Communication Auteurs : Ning Wang, Auteur ; Wengang Zhou, Auteur ; Yibing Song, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 400 - 418 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] corrélation d'images [Termes IGN] détection de cible [Termes IGN] filtre [Termes IGN] objet mobile [Termes IGN] oculométrie [Termes IGN] reconnaissance d'objets [Termes IGN] réseau neuronal siamois [Termes IGN] temps réel [Termes IGN] trajectoire [Termes IGN] vision par ordinateur Résumé : (auteur) The advancement of visual tracking has continuously been brought by deep learning models. Typically, supervised learning is employed to train these models with expensive labeled data. In order to reduce the workload of manual annotation and learn to track arbitrary objects, we propose an unsupervised learning method for visual tracking. The motivation of our unsupervised learning is that a robust tracker should be effective in bidirectional tracking. Specifically, the tracker is able to forward localize a target object in successive frames and backtrace to its initial position in the first frame. Based on such a motivation, in the training process, we measure the consistency between forward and backward trajectories to learn a robust tracker from scratch merely using unlabeled videos. We build our framework on a Siamese correlation filter network, and propose a multi-frame validation scheme and a cost-sensitive loss to facilitate unsupervised learning. Without bells and whistles, the proposed unsupervised tracker achieves the baseline accuracy of classic fully supervised trackers while achieving a real-time speed. Furthermore, our unsupervised framework exhibits a potential in leveraging more unlabeled or weakly labeled data to further improve the tracking accuracy. Numéro de notice : A2021-353 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s11263-020-01357-4 date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.1007/s11263-020-01357-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97604 [article]

Group diagrams for representing trajectories / Maike Buchin in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2401 - 2433 Titre : Group diagrams for representing trajectories Type de document : Article/Communication Auteurs : Maike Buchin, Auteur ; Bernhard Kilgus, Auteur ; Andrea Kölzsch, Auteur Année de publication : 2020 Article en page(s) : pp 2401 - 2433 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] approximation [Termes IGN] diagramme [Termes IGN] distance de Fréchet [Termes IGN] données GPS [Termes IGN] géomètrie algorithmique [Termes IGN] itinéraire [Termes IGN] migration animale [Termes IGN] mouvement [Termes IGN] objet mobile [Termes IGN] trajectoire Résumé : (auteur) Given the trajectories of one or several moving groups, we propose a new framework, the group diagram (GD) for representing these. Specifically, we seek a minimal GD as a concise representation of the groups maintaining the spatio-temporal structure of the groups’ movement. A GD is specified by three input values, namely a distance threshold, a similarity measure and a minimality criterion. For several variants of the GD, we give a comprehensive analysis of their computational complexity and present efficient approximation algorithms for their computation. Furthermore, we experimentally evaluate our algorithms on GPS data of migrating geese. Applying the proposed methods on these data sets reveals how the GD concisely represents the movement of the groups. This representation can be used for further analysis and for the formulation of new hypotheses for further ecological research, such as differences in movement patterns of groups on different surfaces or the shift of migration routes over several years. We use different similarity measures to summarize the migration routes of (i) a goose family for one migration period and to summarize (ii) the migration routes of one individual for several migration periods or (iii) the migration routes of several independent individuals for one migration period. Numéro de notice : A2020-690 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1684498 date de publication en ligne : 25/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1684498 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96227 [article]

Semantic trajectory segmentation based on change-point detection and ontology / Yuan Gao in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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A framework for group converging pattern mining using spatiotemporal trajectories / Bin Zhao in Geoinformatica [en ligne], vol 24 n° 4 (October 2020)  

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Incorporating behavior into animal movement modeling: a constrained agent-based model for estimating visit probabilities in space-time prisms / Rebecca W. Loraamm in International journal of geographical information science IJGIS, vol 34 n° 8 (August 2020)  

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An empirical study on the intra-urban goods movement patterns using logistics big data / Pengxiang Zhao in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

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Uber movement data: a proxy for average one-way commuting times by car / Yeran Sun in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)  

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Distributed and parallel architectures for spatial data / Alberto Belussi (2020)  

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Moving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans / Teng Wu (2020)  

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A polyhedra-based model for moving regions in databases / Florian Heinz in International journal of geographical information science IJGIS, vol 34 n° 1 (January 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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SMSM: a similarity measure for trajectory stops and moves / Andre L. Lehmann in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

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