Details
| Original language | English |
|---|---|
| Pages (from-to) | 21-26 |
| Number of pages | 6 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 42 |
| Issue number | 2/W13 |
| Publication status | Published - 4 Jun 2019 |
| Event | 4th ISPRS Geospatial Week 2019 - Enschede, Netherlands Duration: 10 Jun 2019 → 14 Jun 2019 |
Abstract
LiDAR systems are frequently used for driver assistance systems. The minimal distance to other objects and the exact pose of a vehicle is important for ego movement prediction. Therefore, in this work, we extract the poses of vehicles from LiDAR point clouds. To this end, we measure them with LiDAR, segment the vehicle points and extract the pose. Further, we analyze the influence of LiDAR resolutions on the pose extraction by active shape models (ASM) and by the center of bounding boxes combined with the principal component analysis (BC-PCA).
Keywords
- Active Shape Model, LiDAR, Point Cloud, Pose Estimation, Segmentation, Vehicle Detection
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 42, No. 2/W13, 04.06.2019, p. 21-26.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Active shape model precision analysis of vehicle detection in 3D lidar point clouds
AU - Busch, S.
N1 - Funding Information: This work was funded by the German Science Foundation DFG within the priority programme SPP 1835, “Cooperative Interacting Automobiles”.
PY - 2019/6/4
Y1 - 2019/6/4
N2 - LiDAR systems are frequently used for driver assistance systems. The minimal distance to other objects and the exact pose of a vehicle is important for ego movement prediction. Therefore, in this work, we extract the poses of vehicles from LiDAR point clouds. To this end, we measure them with LiDAR, segment the vehicle points and extract the pose. Further, we analyze the influence of LiDAR resolutions on the pose extraction by active shape models (ASM) and by the center of bounding boxes combined with the principal component analysis (BC-PCA).
AB - LiDAR systems are frequently used for driver assistance systems. The minimal distance to other objects and the exact pose of a vehicle is important for ego movement prediction. Therefore, in this work, we extract the poses of vehicles from LiDAR point clouds. To this end, we measure them with LiDAR, segment the vehicle points and extract the pose. Further, we analyze the influence of LiDAR resolutions on the pose extraction by active shape models (ASM) and by the center of bounding boxes combined with the principal component analysis (BC-PCA).
KW - Active Shape Model
KW - LiDAR
KW - Point Cloud
KW - Pose Estimation
KW - Segmentation
KW - Vehicle Detection
UR - http://www.scopus.com/inward/record.url?scp=85067427368&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLII-2-W13-21-2019
DO - 10.5194/isprs-archives-XLII-2-W13-21-2019
M3 - Conference article
AN - SCOPUS:85067427368
VL - 42
SP - 21
EP - 26
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 - 2/W13
T2 - 4th ISPRS Geospatial Week 2019
Y2 - 10 June 2019 through 14 June 2019
ER -