Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bruno Mirbach
Dr. Jason Raphael Rambach
Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal
Sk Aziz Ali
Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Ahmet Firintepe
Sophie Folawiyo
David Michael Fürst
Kamalveerkaur Garewal
Christiano Couto Gava
Leif Eric Goebel
Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi
Jigyasa Singh Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal
Mina Ameli
Nareg Minaskan Karabid
Mohammad Minouei
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
Qinzhuan Qian
Engr. Kumail Raza
Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar
Alexander Schäfer
Pascal Schneider
René Schuster
Mohamed Selim
Lukas Stefan Staecker
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Yaxu Xie
Dr. Vladislav Golyanik
Dr. Aditya Tewari
André Luiz Brandão
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB
IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE Intelligent Transportation Systems Conference (IEEE ITSC-2020) September 20-23 Virtual Conference Greece IEEE 2020 .
- Abstract:
- While short range 3D pedestrian detection is sufficient for emergency breaking, long range detections are required for smooth breaking and gaining trust in autonomous vehicles. The current state-of-the-art on the KITTI benchmark performs suboptimal in detecting the position of pedestrians at long range. Thus, we propose an approach specifically targeting long range 3D pedestrian detection (LRPD), leveraging the density of RGB and the precision of LiDAR. Therefore, for proposals, RGB instance segmentation and LiDAR point based proposal generation are combined, followed by a second stage using both sensor modalities symmetrically. This leads to a significant improvement in mAP on long range compared to the current state-of-the art. The evaluation of our LRPD approach was done on the pedestrians from the KITTI benchmark.