Details
| Original language | English |
|---|---|
| Pages (from-to) | 505-512 |
| Number of pages | 8 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 42 |
| Issue number | 1/W1 |
| Publication status | Published - 31 May 2017 |
| Event | ISPRS Hannover Workshop 2017 on High-Resolution Earth Imaging for Geospatial Information, HRIGI 2017, City Models, Roads and Traffic , CMRT 2017, Image Sequence Analysis, ISA 2017, European Calibration and Orientation Workshop, EuroCOW 2017: HRIGI - High-Resolution Earth Imaging for Geospatial Information, CMRT - City Models, Roads and Traffic, ISA - Image Sequence Analysis, EuroCOW - European Calibration and Orientation Workshop - Hannover, Hannover, Germany Duration: 6 Jun 2017 → 9 Jun 2017 |
Abstract
The detection and pose estimation of vehicles plays an important role for automated and autonomous moving objects e.g. in autonomous driving environments. We tackle that problem on the basis of street level stereo images, obtained from a moving vehicle. Processing every stereo pair individually, our approach is divided into two subsequent steps: the vehicle detection and the modelling step. For the detection, we make use of the 3D stereo information and incorporate geometric assumptions on vehicle inherent properties in a firstly applied generic 3D object detection. By combining our generic detection approach with a state of the art vehicle detector, we are able to achieve satisfying detection results with values for completeness and correctness up to more than 86%. By fitting an object specific vehicle model into the vehicle detections, we are able to reconstruct the vehicles in 3D and to derive pose estimations as well as shape parameters for each vehicle. To deal with the intra-class variability of vehicles, we make use of a deformable 3D active shape model learned from 3D CAD vehicle data in our model fitting approach. While we achieve encouraging values up to 67.2% for correct position estimations, we are facing larger problems concerning the orientation estimation. The evaluation is done by using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012).
Keywords
- 3D modelling, 3D reconstruction, Active shape model, Object detection, Stereo images, Object Detection, Stereo Images, Active Shape Model, 3D Reconstruction, 3D Modelling
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Computer Science(all)
- Information Systems
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 42, No. 1/W1, 31.05.2017, p. 505-512.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - DETECTION AND 3D MODELLING OF VEHICLES FROM TERRESTRIAL STEREO IMAGE PAIRS
AU - Coenen, M.
AU - Rottensteiner, Franz
AU - Heipke, Christian
N1 - Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/5/31
Y1 - 2017/5/31
N2 - The detection and pose estimation of vehicles plays an important role for automated and autonomous moving objects e.g. in autonomous driving environments. We tackle that problem on the basis of street level stereo images, obtained from a moving vehicle. Processing every stereo pair individually, our approach is divided into two subsequent steps: the vehicle detection and the modelling step. For the detection, we make use of the 3D stereo information and incorporate geometric assumptions on vehicle inherent properties in a firstly applied generic 3D object detection. By combining our generic detection approach with a state of the art vehicle detector, we are able to achieve satisfying detection results with values for completeness and correctness up to more than 86%. By fitting an object specific vehicle model into the vehicle detections, we are able to reconstruct the vehicles in 3D and to derive pose estimations as well as shape parameters for each vehicle. To deal with the intra-class variability of vehicles, we make use of a deformable 3D active shape model learned from 3D CAD vehicle data in our model fitting approach. While we achieve encouraging values up to 67.2% for correct position estimations, we are facing larger problems concerning the orientation estimation. The evaluation is done by using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012).
AB - The detection and pose estimation of vehicles plays an important role for automated and autonomous moving objects e.g. in autonomous driving environments. We tackle that problem on the basis of street level stereo images, obtained from a moving vehicle. Processing every stereo pair individually, our approach is divided into two subsequent steps: the vehicle detection and the modelling step. For the detection, we make use of the 3D stereo information and incorporate geometric assumptions on vehicle inherent properties in a firstly applied generic 3D object detection. By combining our generic detection approach with a state of the art vehicle detector, we are able to achieve satisfying detection results with values for completeness and correctness up to more than 86%. By fitting an object specific vehicle model into the vehicle detections, we are able to reconstruct the vehicles in 3D and to derive pose estimations as well as shape parameters for each vehicle. To deal with the intra-class variability of vehicles, we make use of a deformable 3D active shape model learned from 3D CAD vehicle data in our model fitting approach. While we achieve encouraging values up to 67.2% for correct position estimations, we are facing larger problems concerning the orientation estimation. The evaluation is done by using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012).
KW - 3D modelling
KW - 3D reconstruction
KW - Active shape model
KW - Object detection
KW - Stereo images
KW - Object Detection
KW - Stereo Images
KW - Active Shape Model
KW - 3D Reconstruction
KW - 3D Modelling
UR - http://www.scopus.com/inward/record.url?scp=85021070098&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLII-1-W1-505-2017
DO - 10.5194/isprs-archives-XLII-1-W1-505-2017
M3 - Conference article
AN - SCOPUS:85021070098
VL - 42
SP - 505
EP - 512
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - 1/W1
T2 - ISPRS Hannover Workshop 2017 on High-Resolution Earth Imaging for Geospatial Information, HRIGI 2017, City Models, Roads and Traffic , CMRT 2017, Image Sequence Analysis, ISA 2017, European Calibration and Orientation Workshop, EuroCOW 2017
Y2 - 6 June 2017 through 9 June 2017
ER -