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Titre : Change detection from mobile laser scanning point clouds Titre original : Détection de changements à partir de nuages de points de cartographie mobile Type de document : Thèse/HDR Auteurs : Wen Xiao, Auteur ; Nicolas Paparoditis , Directeur de thèse ; Bruno Vallet
, Encadrant
Editeur : Champs/Marne : Université Paris-Est Marne-la-Vallée UPEM Année de publication : 2015 Importance : 110 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat présentée pour obtenir le grade de docteur Université Paris-Est, Ecole Doctorale MSTIC, spécialité Sciences et Technologies de l’Information GéographiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage dirigé
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] instrumentation Riegl
[Termes IGN] objet mobile
[Termes IGN] semis de points
[Termes IGN] véhicule automobileMots-clés libres : occupancy-based method Index. décimale : THESE Thèses et HDR Résumé : (auteur) Mobile mapping systems are increasingly used for street environment mapping, especially mobile laser scanning technology enables precise street mapping, scene understanding, facade modelling, etc. In this research, the change detection from laser scanning point clouds is investigated. First of all, street environment change detection using RIEGL data is studied for the purpose of database updating and temporary object identification. An occupancy-based method is presented to overcome the challenges encountered by the conventional distance-based method, such as occlusion, anisotropic sampling. Occluded areas are identified by modelling the occupancy states within the laser scanning range. The gaps between points and scan lines are interpolated under the sensor reference framework, where the sampling density is isotropic. Even there are some conflicts on penetrable objects, e.g. trees, fences, the occupancy-based method is able to enhance the point-to-triangle distance-based method. The change detection method is also applied to data acquired by different laser scanners at different temporal-scales with the intention to have wider range of applications. The local sensor reference framework is adapted to Velodyne laser scanning geometry. The occupancy-based method is implemented to detection moving objects. Since the method detects the change of each point, moving objects are detect at point level. As the Velodyne scanner constantly scans the surroundings, the trajectories of moving objects can be detected. A simultaneous detection and tracking algorithm is proposed to recover the pedestrian trajectories in order to accurately estimate the traffic flow of pedestrian in public places. Changes can be detected not only at point level, but also at object level. The changes of cars parking on street sides at different times are detected to help regulate on-street car parking since the parking duration is limited. In this case, cars are detected in the first place, then they are compared with corresponding ones. Apart from car changes, parking positions and car types are also important information for parking management. All the processes are solved in a supervised learning framework. Furthermore, a model-based car reconstruction method is proposed to precisely locate cars. The model parameters are also treated as car features for better decision making. Moreover, the geometrically accurate models can be used for visualization purposes. Under the theme of change detection, related topics, e.g. tracking, classification, modelling, are also studied for the reason of practical applications. More importantly, the change detection methods are applied to different data acquisition geometries at multiple temporal-scales. Both bottom-up (point-based) and top-down (object-based) change detection strategies are investigated. Note de contenu : 1 Introduction
1.1 Motivation
1.1.1 Fine-scale change detection
1.1.2 Applications
1.2 Problems
1.3 Mobile mapping system and data
1.3.1 REIGL laser scanner
1.3.2 Velodyne laser scanner
1.4 Objectives
1.5 Structure
1.6 Contribution
2 State-of-the-art
2.1 Change detection methods
2.2 Change detection from imageries
2.3 Change detection from airborne lidar data
2.4 Change detection from terrestrial lidar data
2.5 Change detection in related domains
3 Street Environment Change Detection
3.1 Introduction
3.2 Contribution
3.3 Occupancy-based change detection
3.3.1 Laser Scanning Geometry
3.3.2 Occupancy modelling for an individual ray
3.3.3 Occupancy fusion and corresponding point retrieval
3.3.4 Consistency assessment between different epochs
3.4 Combination with distance-based change detection
3.5 Experiments and result
3.6 Evaluation and discussion
3.7 Conclusion
4 Simultaneous detection and tracking of pedestrian
4.1 Introduction
4.2 Related work
4.2.1 Pedestrian tracking in computer vision
4.2.2 Pedestrian tracking using laser scanning data
4.3 Methodology
4.3.1 Moving object detection
4.3.2 Simultaneous detection and tracking of pedestrian
4.3.3 Optimization
4.4 Experiments and results
4.4.1 Moving object detection
4.4.2 Pedestrian tracking
4.5 Discussions
4.6 Conclusion
5 Street-side car detection, classification and change detection
5.1 Introduction
5.2 Related work
5.2.1 Vehicle detection
5.2.2 Vehicle modelling
5.3 Methodology
5.3.1 Segmentation and feature extraction
5.3.2 Car modelling
5.3.3 Car recognition and localization
5.3.4 Car classification
5.3.5 Change detection
5.4 Experiments and results
5.4.1 Segmentation
5.4.2 Car modelling
5.4.3 Car recognition
5.4.4 Car classification
5.4.5 Change detection
5.5 Conclusion
6 Conclusion and perspectives
6.1 Summary
6.2 Conclusion
6.3 PerspectivesNuméro de notice : 17339 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de doctorat : Sciences et Technologies de l’Information Géographique : Paris-Est : 2015 Organisme de stage : MATIS (IGN) nature-HAL : Thèse DOI : sans En ligne : https://theses.hal.science/tel-01373359 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83191