Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems |
| Untertitel | Smart Mobility for Safety and Sustainability, ITSC 2015 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 2812-2818 |
| Seitenumfang | 7 |
| Publikationsstatus | Veröffentlicht - 30 Okt. 2015 |
| Veranstaltung | 18th IEEE International Conference on Intelligent Transportation Systems, ITSC 2015 - Gran Canaria, Spanien Dauer: 15 Sept. 2015 → 18 Sept. 2015 |
Abstract
This paper presents a procedure to extract parking statistics from a 3D point cloud recorded with two 2D LiDAR sensors mounted on a vehicle. Policy makers can use these parking statistics to reduce parking search traffic in cities by identifying parking characteristics and adjusting current parking rules and policies. The extraction procedure basically consists of an object segmentation and a classification step. For object segmentation, a region growing approach is used to extract the ground surface and to separate distinct objects. For object classification, a random forest classifier is employed with various local and global point features to identify the characteristic shape of vehicles. Comparing the point clouds of both LiDAR scanners allows the exclusion of moving vehicles from the result. A second segmentation in a finer raster after classification is used to reduce the occurrence of undersegmentation. The procedure is evaluated on a 5.5 km track including a residential and a commercial district with parallel and perpendicular parking in a large city in Germany. The results reveal reliable detection of parked vehicles in most situations and therefore approve its suitability for parking studies. Multiple statistics like vehicle dimensions, parking gaps and temporal behavior can be extracted from this procedure. As an example, the occupancy of street segments in the course of one day is presented.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Fahrzeugbau
- Ingenieurwesen (insg.)
- Maschinenbau
- Informatik (insg.)
- Angewandte Informatik
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Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems: Smart Mobility for Safety and Sustainability, ITSC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. S. 2812-2818 7313544.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - On-street Parking Statistics Using LiDAR Mobile Mapping
AU - Bock, Fabian
AU - Eggert, Daniel
AU - Sester, Monika
PY - 2015/10/30
Y1 - 2015/10/30
N2 - This paper presents a procedure to extract parking statistics from a 3D point cloud recorded with two 2D LiDAR sensors mounted on a vehicle. Policy makers can use these parking statistics to reduce parking search traffic in cities by identifying parking characteristics and adjusting current parking rules and policies. The extraction procedure basically consists of an object segmentation and a classification step. For object segmentation, a region growing approach is used to extract the ground surface and to separate distinct objects. For object classification, a random forest classifier is employed with various local and global point features to identify the characteristic shape of vehicles. Comparing the point clouds of both LiDAR scanners allows the exclusion of moving vehicles from the result. A second segmentation in a finer raster after classification is used to reduce the occurrence of undersegmentation. The procedure is evaluated on a 5.5 km track including a residential and a commercial district with parallel and perpendicular parking in a large city in Germany. The results reveal reliable detection of parked vehicles in most situations and therefore approve its suitability for parking studies. Multiple statistics like vehicle dimensions, parking gaps and temporal behavior can be extracted from this procedure. As an example, the occupancy of street segments in the course of one day is presented.
AB - This paper presents a procedure to extract parking statistics from a 3D point cloud recorded with two 2D LiDAR sensors mounted on a vehicle. Policy makers can use these parking statistics to reduce parking search traffic in cities by identifying parking characteristics and adjusting current parking rules and policies. The extraction procedure basically consists of an object segmentation and a classification step. For object segmentation, a region growing approach is used to extract the ground surface and to separate distinct objects. For object classification, a random forest classifier is employed with various local and global point features to identify the characteristic shape of vehicles. Comparing the point clouds of both LiDAR scanners allows the exclusion of moving vehicles from the result. A second segmentation in a finer raster after classification is used to reduce the occurrence of undersegmentation. The procedure is evaluated on a 5.5 km track including a residential and a commercial district with parallel and perpendicular parking in a large city in Germany. The results reveal reliable detection of parked vehicles in most situations and therefore approve its suitability for parking studies. Multiple statistics like vehicle dimensions, parking gaps and temporal behavior can be extracted from this procedure. As an example, the occupancy of street segments in the course of one day is presented.
KW - LiDAR
KW - Mobile mapping
KW - Parking statistics
KW - Parking survey
KW - Vehicle detection
UR - http://www.scopus.com/inward/record.url?scp=84950267543&partnerID=8YFLogxK
U2 - 10.1109/itsc.2015.452
DO - 10.1109/itsc.2015.452
M3 - Conference contribution
AN - SCOPUS:84950267543
SP - 2812
EP - 2818
BT - Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Intelligent Transportation Systems, ITSC 2015
Y2 - 15 September 2015 through 18 September 2015
ER -