Vehicle Detection in Urban Environments Scanned by a Lidar
Detection of vehicles on 3D point clouds is performed by using the algorithm presented in this work. Point clouds correspond to urban environments and were acquired with the LIDAR Velodyne HDL-64E. The environment is considered semi-structured so that can be modeled using planes. Vehicle detection...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | Spanish |
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Universitat Politècnica de València
2015-04-01
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| Series: | Revista Iberoamericana de Automática e Informática Industrial RIAI |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/RIAI/article/view/9392 |
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| _version_ | 1846094857744941056 |
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| author | Alfonso Ramírez Pedraza José Joel González Barbosa Francisco Javier Ornelas Rodríguez Ángel Iván García Moreno Adan Salazar Garibay Erick Alejandro González Barbosa |
| author_facet | Alfonso Ramírez Pedraza José Joel González Barbosa Francisco Javier Ornelas Rodríguez Ángel Iván García Moreno Adan Salazar Garibay Erick Alejandro González Barbosa |
| author_sort | Alfonso Ramírez Pedraza |
| collection | DOAJ |
| description | Detection of vehicles on 3D point clouds is performed by using the algorithm presented in this work. Point clouds correspond to urban environments and were acquired with the LIDAR Velodyne HDL-64E. The environment is considered semi-structured so that can be modeled using planes.
Vehicle detection is carried out on to stages, segmentation and indexation. First stage is at the same time composed of three sub-stages. In the first one the principal plane (in this case the floor) is extracted, in the second sub-stage secondary planes are extracted using a tailored version of Hough's method, secondary planes are those perpendicular to the main plane. Finally in the third sub-stage and using MeanShift method, the remaining objects are segmented.
Indexation on its side is divided into two sub-stages, in the first one, last segmented objects using MeanShift method are modeled using histograms according to the direction of the object's 3D points normal; in the second stage histograms are compared to those previously stored on a database of object's histograms. Optimizing of detection thresholds was carried out through ROC analysis. Two databases were used during the experiments, the first DB have 4500 objects and was used for ROC analysis training; the second one contained 3000 objects and was used for verification. |
| format | Article |
| id | doaj-art-71e4f03b369d40d1b006c2d33bb97298 |
| institution | Kabale University |
| issn | 1697-7912 1697-7920 |
| language | Spanish |
| publishDate | 2015-04-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | Revista Iberoamericana de Automática e Informática Industrial RIAI |
| spelling | doaj-art-71e4f03b369d40d1b006c2d33bb972982025-01-02T12:11:53ZspaUniversitat Politècnica de ValènciaRevista Iberoamericana de Automática e Informática Industrial RIAI1697-79121697-79202015-04-0112218919810.1016/j.riai.2015.03.0036427Vehicle Detection in Urban Environments Scanned by a LidarAlfonso Ramírez Pedraza0José Joel González Barbosa1Francisco Javier Ornelas Rodríguez2Ángel Iván García Moreno3Adan Salazar Garibay4Erick Alejandro González Barbosa5Instituto Politécnico NacionalInstituto Politécnico NacionalInstituto Politécnico NacionalInstituto Politécnico NacionalQuantificare S.A.Instituto Tecnológico Superior de IrapuatoDetection of vehicles on 3D point clouds is performed by using the algorithm presented in this work. Point clouds correspond to urban environments and were acquired with the LIDAR Velodyne HDL-64E. The environment is considered semi-structured so that can be modeled using planes. Vehicle detection is carried out on to stages, segmentation and indexation. First stage is at the same time composed of three sub-stages. In the first one the principal plane (in this case the floor) is extracted, in the second sub-stage secondary planes are extracted using a tailored version of Hough's method, secondary planes are those perpendicular to the main plane. Finally in the third sub-stage and using MeanShift method, the remaining objects are segmented. Indexation on its side is divided into two sub-stages, in the first one, last segmented objects using MeanShift method are modeled using histograms according to the direction of the object's 3D points normal; in the second stage histograms are compared to those previously stored on a database of object's histograms. Optimizing of detection thresholds was carried out through ROC analysis. Two databases were used during the experiments, the first DB have 4500 objects and was used for ROC analysis training; the second one contained 3000 objects and was used for verification.https://polipapers.upv.es/index.php/RIAI/article/view/9392Nube de Puntos 3DLIDARSegmentación 3D |
| spellingShingle | Alfonso Ramírez Pedraza José Joel González Barbosa Francisco Javier Ornelas Rodríguez Ángel Iván García Moreno Adan Salazar Garibay Erick Alejandro González Barbosa Vehicle Detection in Urban Environments Scanned by a Lidar Revista Iberoamericana de Automática e Informática Industrial RIAI Nube de Puntos 3D LIDAR Segmentación 3D |
| title | Vehicle Detection in Urban Environments Scanned by a Lidar |
| title_full | Vehicle Detection in Urban Environments Scanned by a Lidar |
| title_fullStr | Vehicle Detection in Urban Environments Scanned by a Lidar |
| title_full_unstemmed | Vehicle Detection in Urban Environments Scanned by a Lidar |
| title_short | Vehicle Detection in Urban Environments Scanned by a Lidar |
| title_sort | vehicle detection in urban environments scanned by a lidar |
| topic | Nube de Puntos 3D LIDAR Segmentación 3D |
| url | https://polipapers.upv.es/index.php/RIAI/article/view/9392 |
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