Descripteur
Termes IGN > informatique > base de données > base de données orientée objet > base de données d'objets mobiles
base de données d'objets mobilesVoir aussi |
Documents disponibles dans cette catégorie (169)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
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)
[article]
Titre : SMSM: a similarity measure for trajectory stops and moves Type de document : Article/Communication Auteurs : Andre L. Lehmann, Auteur ; Luis Otavio Alvares, Auteur ; Vania Bogorny, Auteur Année de publication : 2019 Article en page(s) : pp 1847 - 1872 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] calcul d'itinéraire
[Termes IGN] durée de trajet
[Termes IGN] information sémantique
[Termes IGN] mesure de similitude
[Termes IGN] objet mobile
[Termes IGN] relation sémantique
[Termes IGN] taxi
[Termes IGN] trajet (mobilité)Résumé : (auteur) For many years trajectory similarity research has focused on raw trajectories, considering only space and time information. With the trajectory semantic enrichment, emerged the need for similarity measures that support space, time, and semantics. Although some trajectory similarity measures deal with all these dimensions, they consider only stops, ignoring the moves. We claim that, for some applications, the movement between stops is as important as the stops, and they must be considered in the similarity analysis. In this article, we propose SMSM, a novel similarity measure for semantic trajectories that considers both stops and moves. We evaluate SMSM with three trajectory datasets: (i) a synthetic trajectory dataset generated with the Hermoupolis semantic trajectory generator, (ii) a real trajectory dataset from the CRAWDAD project, and (iii) the Geolife dataset. The results show that SMSM overcomes state-of-the-art measures developed either for raw or semantic trajectories. Numéro de notice : A2019-391 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1605074 Date de publication en ligne : 24/06/2019 En ligne : https://doi.org/10.1080/13658816.2019.1605074 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93486
in International journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1847 - 1872[article]Réservation
Réserver ce documentExemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019092 RAB Revue Centre de documentation En réserve L003 Disponible Relative space-based GIS data model to analyze the group dynamics of moving objects / Mingxiang Feng in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)
[article]
Titre : Relative space-based GIS data model to analyze the group dynamics of moving objects Type de document : Article/Communication Auteurs : Mingxiang Feng, Auteur ; Shih-Lung Shaw, Auteur ; Zhixiang Fang, Auteur ; Hao Cheng, Auteur Année de publication : 2019 Article en page(s) : pp 74 - 95 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] analyse spatio-temporelle
[Termes IGN] base de données d'objets mobiles
[Termes IGN] base de données orientée objet
[Termes IGN] modèle conceptuel de données
[Termes IGN] objet mobile
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] SIG dynamique
[Termes IGN] UMLRésumé : (Auteur) The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects’ relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5–50% those of the absolute GIS method for the same function of these three scenarios. It’s better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service. Numéro de notice : A2019-261 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.002 Date de publication en ligne : 15/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93074
in ISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 74 - 95[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Patch-based detection of dynamic objects in CrowdCam images / Gagan Kanojia in The Visual Computer, vol 35 n° 4 (April 2019)
[article]
Titre : Patch-based detection of dynamic objects in CrowdCam images Type de document : Article/Communication Auteurs : Gagan Kanojia, Auteur ; Shanmuganathan Raman, Auteur Année de publication : 2019 Article en page(s) : pp 521 - 534 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] compréhension de l'image
[Termes IGN] détection d'objet
[Termes IGN] géométrie épipolaire
[Termes IGN] objet mobile
[Termes IGN] objet statiqueRésumé : (auteur) A scene can be divided into two parts: static and dynamic. The parts of the scene which do not admit any motion are static regions, while moving objects correspond to dynamic regions. In this work, we tackle the challenging task of identifying dynamic objects present in the CrowdCam images. Our approach exploits the coherency present in the natural images and utilizes the epipolar geometry present between a pair of images to achieve this objective. It does not require a dynamic object to be present in all the given images. We show that the proposed approach obtains state-of-the-art accuracy on standard datasets. Numéro de notice : A2019-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00371-018-1480-3 Date de publication en ligne : 06/02/2018 En ligne : https://doi.org/10.1007/s00371-018-1480-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92442
in The Visual Computer > vol 35 n° 4 (April 2019) . - pp 521 - 534[article]A conceptual framework for studying collective reactions to events in location-based social media / Alexander Dunkel in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)
[article]
Titre : A conceptual framework for studying collective reactions to events in location-based social media Type de document : Article/Communication Auteurs : Alexander Dunkel, Auteur ; Gennady Andrienko, Auteur ; Natalia Andrienko, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 780 - 804 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse comparative
[Termes IGN] diffusion de l'information
[Termes IGN] données spatiotemporelles
[Termes IGN] événement
[Termes IGN] hypercube
[Termes IGN] information géographique
[Termes IGN] modèle conceptuel de données
[Termes IGN] réseau social
[Termes IGN] traitement interactifRésumé : (Auteur) Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events. Numéro de notice : A2019-216 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1546390 Date de publication en ligne : 18/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1546390 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92688
in International journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 780 - 804[article]Réservation
Réserver ce documentExemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019032 RAB Revue Centre de documentation En réserve L003 Disponible Learning to segment moving objects / Pavel Tokmakov in International journal of computer vision, vol 127 n° 3 (March 2019)
[article]
Titre : Learning to segment moving objects Type de document : Article/Communication Auteurs : Pavel Tokmakov, Auteur ; Cordelia Schmid, Auteur ; Karteek Alahari, Auteur Année de publication : 2019 Article en page(s) : pp 282 - 301 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] cohérence temporelle
[Termes IGN] image vidéo
[Termes IGN] objet mobile
[Termes IGN] reconnaissance d'objets
[Termes IGN] réseau neuronal convolutif
[Termes IGN] séquence d'imagesRésumé : (Auteur) We study the problem of segmenting moving objects in unconstrained videos. Given a video, the task is to segment all the objects that exhibit independent motion in at least one frame. We formulate this as a learning problem and design our framework with three cues: (1) independent object motion between a pair of frames, which complements object recognition, (2) object appearance, which helps to correct errors in motion estimation, and (3) temporal consistency, which imposes additional constraints on the segmentation. The framework is a two-stream neural network with an explicit memory module. The two streams encode appearance and motion cues in a video sequence respectively, while the memory module captures the evolution of objects over time, exploiting the temporal consistency. The motion stream is a convolutional neural network trained on synthetic videos to segment independently moving objects in the optical flow field. The module to build a “visual memory” in video, i.e., a joint representation of all the video frames, is realized with a convolutional recurrent unit learned from a small number of training video sequences. For every pixel in a frame of a test video, our approach assigns an object or background label based on the learned spatio-temporal features as well as the “visual memory” specific to the video. We evaluate our method extensively on three benchmarks, DAVIS, Freiburg-Berkeley motion segmentation dataset and SegTrack. In addition, we provide an extensive ablation study to investigate both the choice of the training data and the influence of each component in the proposed framework. Numéro de notice : A2018-601 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1122-2 Date de publication en ligne : 22/09/2018 En ligne : https://doi.org/10.1007/s11263-018-1122-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92528
in International journal of computer vision > vol 127 n° 3 (March 2019) . - pp 282 - 301[article]Point clouds for direct pedestrian pathfinding in urban environments / Jesus Balado in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkRobust vehicle detection in aerial images using bag-of-words and orientation aware scanning / Hailing Zhou in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkA data model for moving regions of fixed shape in databases / Florian Heinz in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkA context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks / Shaohua Wang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkUsing interactions and dynamics for mining groups of moving objects from trajectory data / Corrado Loglisci in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkRange-image: Incorporating sensor topology for lidar point cloud processing / Pierre Biasutti in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)PermalinkAttribute trajectory analysis : a framework to analyse attribute changes using trajectory analysis techniques / Long Zhang in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)PermalinkPermalinkOccupancy modelling for moving object detection from Lidar point clouds: A comparative study / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W4 (September 2017)PermalinkThe geometry of space-time prisms with uncertain anchors / Bart Kuijpers in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)Permalink