Dual-Branch CNNs for Vehicle Detection and Tracking on LiDAR Data
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Título: | Dual-Branch CNNs for Vehicle Detection and Tracking on LiDAR Data |
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Autor/es: | Vaquero, Víctor | del Pino, Iván | Moreno-Noguer, Francesc | Solà, Joan | Sanfeliu, Alberto | Andrade-Cetto, Juan |
Grupo/s de investigación o GITE: | Automática, Robótica y Visión Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Deep convolutional neural network | Vehicle detection and tracking | LiDAR | Point cloud |
Área/s de conocimiento: | Ingeniería de Sistemas y Automática |
Fecha de publicación: | 16-jul-2020 |
Editor: | IEEE |
Cita bibliográfica: | IEEE Transactions on Intelligent Transportation Systems. 2021, 22(11): 6942-6953. https://doi.org/10.1109/TITS.2020.2998771 |
Resumen: | We present a novel vehicle detection and tracking system that works solely on 3D LiDAR information. Our approach segments vehicles using a dual-view representation of the 3D LiDAR point cloud on two independently trained convolutional neural networks, one for each view. A bounding box growing algorithm is applied to the fused output of the networks to properly enclose the segmented vehicles. Bounding boxes are grown using a probabilistic method that takes into account also occluded areas. The final vehicle bounding boxes act as observations for a multi-hypothesis tracking system which allows to estimate the position and velocity of the observed vehicles. We thoroughly evaluate our system on the KITTI benchmarks both for detection and tracking separately and show that our dual-branch classifier consistently outperforms previous single-branch approaches, improving or directly competing to other state of the art LiDAR-based methods. |
Patrocinador/es: | This work was supported in part by the EU Project LOGIMATIC under Grant H2020-Galileo-2015-1-687534, in part by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI under Grant MDM-2016-0656, in part by the ColRobTransp Project under Grant DPI2016-78957-RAEI/FEDER EU, in part by the EB-SLAM Project under Grant DPI2017-89564-P, and in part by the FPU Grant under Grant FPU15/04446. |
URI: | http://hdl.handle.net/10045/114412 |
ISSN: | 1524-9050 (Print) | 1558-0016 (Online) |
DOI: | 10.1109/TITS.2020.2998771 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1109/TITS.2020.2998771 |
Aparece en las colecciones: | Investigaciones financiadas por la UE INV - AUROVA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Vaquero_etal_2020_IEEE-TITS_accepted.pdf | Accepted Manuscript (acceso abierto) | 10,13 MB | Adobe PDF | Abrir Vista previa |
Vaquero_etal_2021_IEEE-TITS_final.pdf | Versión final (acceso restringido) | 9,96 MB | Adobe PDF | Abrir Solicitar una copia |
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