Towards a 3D network model for indoor navigation

2011 ◽  
pp. 85-100 ◽  
Keyword(s):  
Network Model ◽  
Indoor Navigation ◽  
3D Network

Reconstruction of 3D Network Model through CT Scanning

2007 ◽  
Author(s):  
Jian Hou ◽  
ShunKang Zhang ◽  
Yubin Li
Keyword(s):  
Network Model ◽  
Ct Scanning ◽  
3D Network

3D network model of NO transport in tissue

2011 ◽  
Vol 49 (6) ◽  
pp. 633-647 ◽  
Author(s):  
Xuewen Chen ◽  
Donald G. Buerk ◽  
Kenneth A. Barbee ◽  
Patrick Kirby ◽  
Dov Jaron
Keyword(s):  
Network Model ◽  
3D Network

scholarly journals MOVING HUMAN PATH TRACKING BASED ON VIDEO SURVEILLANCE IN 3D INDOOR SCENARIOS

2016 ◽  
Vol III-4 ◽  
pp. 97-101 ◽  
Author(s):  
Yan Zhou ◽  
Sisi Zlatanova ◽  
Zhe Wang ◽  
Yeting Zhang ◽  
Liu Liu
Keyword(s):  
Video Surveillance ◽  
Network Model ◽  
Path Tracking ◽  
Depth Information ◽  
Surveillance Systems ◽  
Indoor Space ◽  
Single Person ◽  
3D Network ◽  
Indoor Scenarios ◽  
Moving Path

Video surveillance systems are increasingly used for a variety of 3D indoor applications. We can analyse human behaviour, discover and avoid crowded areas, monitor human traffic and so forth. In this paper we concentrate on use of surveillance cameras to track and reconstruct the path a person has followed. For the purpose we integrated video surveillance data with a 3D indoor model of the building and develop a single human moving path tracking method. We process the surveillance videos to detected single human moving traces; then we match the depth information of 3D scenes to the constructed 3D indoor network model and define the human traces in the 3D indoor space. Finally, the single human traces extracted from multiple cameras are connected with the help of the connectivity provided by the 3D network model. Using this approach, we can reconstruct the entire walking path. The provided experiments with a single person have verified the effectiveness and robustness of the method.

scholarly journals GEOMETRICAL NETWORK MODEL GENERATION USING POINT CLOUD DATA FOR INDOOR NAVIGATION

2018 ◽  
Vol IV-4 ◽  
pp. 141-146 ◽  
Author(s):  
M. Nakagawa ◽  
R. Nozaki
Keyword(s):  
Network Model ◽  
Point Cloud ◽  
Optimal Path ◽  
Three Dimensional ◽  
Point Clouds ◽  
Indoor Navigation ◽  
Model Generation ◽  
Indoor Environments ◽  
Point Cloud Data ◽  
Cloud Data

<p><strong>Abstract.</strong> Three-dimensional indoor navigation requires various functions, such as the shortest path retrieval, obstacle avoidance, and secure path retrieval, for optimal path finding using a geometrical network model. Although the geometrical network model can be prepared manually, the model should be automatically generated using images and point clouds to represent changing indoor environments. Thus, we propose a methodology for generating a geometrical network model for indoor navigation using point clouds through object classification, navigable area estimation, and navigable path estimation. Our proposed methodology was evaluated through experiments using the benchmark of the International Society for Photogrammetry and Remote Sensing for indoor modeling. In our experiments, we confirmed that our methodology can generate a geometrical network model automatically.</p>

scholarly journals MOVING HUMAN PATH TRACKING BASED ON VIDEO SURVEILLANCE IN 3D INDOOR SCENARIOS

2016 ◽  
Vol III-4 ◽  
pp. 97-101
Author(s):  
Yan Zhou ◽  
Sisi Zlatanova ◽  
Zhe Wang ◽  
Yeting Zhang ◽  
Liu Liu
Keyword(s):  
Video Surveillance ◽  
Network Model ◽  
Path Tracking ◽  
Depth Information ◽  
Surveillance Systems ◽  
Indoor Space ◽  
Single Person ◽  
3D Network ◽  
Indoor Scenarios ◽  
Moving Path

Video surveillance systems are increasingly used for a variety of 3D indoor applications. We can analyse human behaviour, discover and avoid crowded areas, monitor human traffic and so forth. In this paper we concentrate on use of surveillance cameras to track and reconstruct the path a person has followed. For the purpose we integrated video surveillance data with a 3D indoor model of the building and develop a single human moving path tracking method. We process the surveillance videos to detected single human moving traces; then we match the depth information of 3D scenes to the constructed 3D indoor network model and define the human traces in the 3D indoor space. Finally, the single human traces extracted from multiple cameras are connected with the help of the connectivity provided by the 3D network model. Using this approach, we can reconstruct the entire walking path. The provided experiments with a single person have verified the effectiveness and robustness of the method.

scholarly journals 3D NETWORK ANALYSIS FOR INDOOR SPACE APPLICATIONS

2016 ◽  
Vol XLII-2/W2 ◽  
pp. 147-154 ◽  
Author(s):  
E. Tsiliakou ◽  
E. Dimopoulou
Keyword(s):  
Network Analysis ◽  
Structural Complexity ◽  
Indoor Navigation ◽  
3D Analysis ◽  
Space Applications ◽  
Indoor Space ◽  
3D Network ◽  
Space Modeling ◽  
Outdoor Environments ◽  
Definition Of

Indoor space differs from outdoor environments, since it is characterized by a higher level of structural complexity, geometry, as well as topological relations. Indoor space can be considered as the most important component in a building’s conceptual modelling, on which applications such as indoor navigation, routing or analysis are performed. Therefore, the conceptual meaning of sub spaces or the activities taking place in physical building boundaries (e.g. walls), require the comprehension of the building’s indoor hierarchical structure. The scope of this paper is to perform 3D network analysis in a building’s interior and is structured as follows: In Section 1 the definition of indoor space is provided and indoor navigation requirements are analysed. Section 2 describes the processes of indoor space modeling, as well as routing applications. In Section 3, a case study is examined involving a 3D building model generated in CityEngine (exterior shell) and ArcScene (interior parts), in which the use of commercially available software tools (ArcGIS, ESRI), in terms of indoor routing and 3D network analysis, are explored. The fundamentals of performing 3D analysis with the ArcGIS Network Analyst extension were tested. Finally a geoprocessing model was presented, which was specifically designed to be used to interactively find the best route in ArcScene. The paper ends with discussion and concluding remarks on Section 4.

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