TY - JOUR
T1 - Automobile indexation from 3D point clouds of urban scenarios
AU - Alfonso, Ramirez Pedraza
AU - José-Joel, González Barbosa
AU - Raymundo, Ramirez Pedraza
AU - Erick-Alejandro, González Barbosa
AU - Juan-Bautista, Hurtado Ramos
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - In this paper, we introduce a methodology for the detection and segmentation of automobiles in urban scenarios. We use the LiDAR Velodyne HDL-64E to scan the surroundings. The method is comprised of three steps: (1) remove facades, ground plan, and unstructured objects, (2) smoothing data using robust principal component analysis (RPCA), and finally, (3) unstructured objects model and indexing. The dataset is partitioned into training with 4500 objects and test with 3000 objects. Mean Shift thresholds, the filter, the Delaunay parameters, and the histogram modelling are optimized via ROC analysis. It is observed that the car scan quality affects our method to a lesser degree when compared with state-of-the-art methods.
AB - In this paper, we introduce a methodology for the detection and segmentation of automobiles in urban scenarios. We use the LiDAR Velodyne HDL-64E to scan the surroundings. The method is comprised of three steps: (1) remove facades, ground plan, and unstructured objects, (2) smoothing data using robust principal component analysis (RPCA), and finally, (3) unstructured objects model and indexing. The dataset is partitioned into training with 4500 objects and test with 3000 objects. Mean Shift thresholds, the filter, the Delaunay parameters, and the histogram modelling are optimized via ROC analysis. It is observed that the car scan quality affects our method to a lesser degree when compared with state-of-the-art methods.
KW - 3D points cloud
KW - Automobile indexation
KW - indexing
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=85109176907&partnerID=8YFLogxK
U2 - 10.1080/00051144.2021.1947609
DO - 10.1080/00051144.2021.1947609
M3 - Artículo
AN - SCOPUS:85109176907
SN - 0005-1144
VL - 62
SP - 311
EP - 318
JO - Automatika
JF - Automatika
IS - 3
ER -