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est un bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences / International society for photogrammetry and remote sensing (1980 -) (2012 - )
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Ajouter le résultat dans votre panierSimultaneous detection and tracking of pedestrian from panoramic laser scanning data / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)
[article]
Titre : Simultaneous detection and tracking of pedestrian from panoramic laser scanning data Type de document : Article/Communication Auteurs : Wen Xiao, Auteur ; Bruno Vallet , Auteur ; Konrad Schindler, Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2016 Projets : 1-Pas de projet / Article en page(s) : pp 295 - 302 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification barycentrique
[Termes IGN] détection d'objet
[Termes IGN] détection de piéton
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] objet mobile
[Termes IGN] piéton
[Termes IGN] trafic
[Termes IGN] trajet (mobilité)Résumé : (auteur) Pedestrian traffic flow estimation is essential for public place design and construction planning. Traditional data collection by human investigation is tedious, inefficient and expensive. Panoramic laser scanners, e.g. Velodyne HDL-64E, which scan surroundings repetitively at a high frequency, have been increasingly used for 3D object tracking. In this paper, a simultaneous detection and tracking (SDAT) method is proposed for precise and automatic pedestrian trajectory recovery. First, the dynamic environment is detected using two different methods, Nearest-point and Max-distance. Then, all the points on moving objects are transferred into a space-time (x, y, t) coordinate system. The pedestrian detection and tracking amounts to assign the points belonging to pedestrians into continuous trajectories in space-time. We formulate the point assignment task as an energy function which incorporates the point evidence, trajectory number, pedestrian shape and motion. A low energy trajectory will well explain the point observations, and have plausible trajectory trend and length. The method inherently filters out points from other moving objects and false detections. The energy function is solved by a two-step optimization process: tracklet detection in a short temporal window; and global tracklet association through the whole time span. Results demonstrate that the proposed method can automatically recover the pedestrians trajectories with accurate positions and low false detections and mismatches. Numéro de notice : A2016-818 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-III-3-295-2016 Date de publication en ligne : 06/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-annals-III-3-295-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82619
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol III-3 (July 2016) . - pp 295 - 302[article]Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)
[article]
Titre : Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas Type de document : Article/Communication Auteurs : Alexandre Hervieu , Auteur ; Arnaud Le Bris , Auteur ; Clément Mallet , Auteur Année de publication : 2016 Projets : HYEP / Weber, Christiane Article en page(s) : pp 457 - 464 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] méthode de réduction d'énergie
[Termes IGN] occupation du sol
[Termes IGN] optimisation (mathématiques)
[Termes IGN] zone urbaineRésumé : (auteur) An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data,usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispectral images. More specifically, the proposed method takes into account probability class membership maps in order to improve the classification fusion process. Such probability maps are available using standard classification techniques such as Random Forests or Support Vector Machines. Classification probability maps are integrated into an energy framework where minimization of a given energy leads to better classification maps. The energy is minimized using a graph-cut method called quadratic pseudo-boolean optimization (QPBO) with α-expansion. A first model is proposed that gives satisfactory results in terms of classification results and visual interpretation. This model is compared to a standard Potts models adapted to the considered problem. Finally, the model is enhanced by integrating the spatial contrast observed in the data source of higher spatial resolution (i.e., the multispectral image). Obtained results using the proposed energetical decision fusion process are shown on two urban multispectral/hyperspectral datasets. 2-3% improvement is noticed with respect to a Potts formulation and 3-8% compared to a single hyperspectral-based classification. Numéro de notice : A2016-826 Affiliation des auteurs : LASTIG MATIS (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-III-3-457-2016 Date de publication en ligne : 06/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-annals-III-3-457-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82697
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol III-3 (July 2016) . - pp 457 - 464[article]Documents numériques
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