Pedestrian detection and classification ...
Document type :
Communication dans un congrès avec actes
Title :
Pedestrian detection and classification for autonomous train
Author(s) :
Mahtani, Ankur [Auteur]
L'Institut de Recherche Technologique (IRT) de la filière Ferroviaire [IRT Railenium]
Ben Messaoud, Wael [Auteur]
L'Institut de Recherche Technologique (IRT) de la filière Ferroviaire [IRT Railenium]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 [IEMN-DOAE]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Niar, Smail [Auteur]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
Strauss, Clément [Auteur]
L'Institut de Recherche Technologique (IRT) de la filière Ferroviaire [IRT Railenium]
L'Institut de Recherche Technologique (IRT) de la filière Ferroviaire [IRT Railenium]
Ben Messaoud, Wael [Auteur]
L'Institut de Recherche Technologique (IRT) de la filière Ferroviaire [IRT Railenium]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 [IEMN-DOAE]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Niar, Smail [Auteur]
Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
Strauss, Clément [Auteur]
L'Institut de Recherche Technologique (IRT) de la filière Ferroviaire [IRT Railenium]
Conference title :
IEEE 4th International Conference on Image Processing, Applications and Systems, IPAS 2020
City :
Genova
Country :
Italie
Start date of the conference :
2020-12-09
Journal title :
Proceedings of the 4th International Conference on Image Processing, Applications and Systems, IPAS 2020
Publisher :
IEEE
English keyword(s) :
Support vector machines
Location awareness
Embedded systems
Rail transportation
Real-time systems
Location awareness
Embedded systems
Rail transportation
Real-time systems
HAL domain(s) :
Sciences de l'ingénieur [physics]
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Electronique
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Electronique
English abstract : [en]
In this paper, we present a combined approach for human localization and classification in Autonomous Train application. Our contribution is threefold. (a) The creation of a new dataset for workers wearing orange vests in ...
Show more >In this paper, we present a combined approach for human localization and classification in Autonomous Train application. Our contribution is threefold. (a) The creation of a new dataset for workers wearing orange vests in a railway environment context. (b) A deep learning supervised YOLO object detector for persons detection combined with a linear SVM (Support Vector Machine) classifier for persons classification into workers wearing orange vests or travelers. (c) A realtime vision-based technique for the environment monitoring in a driverless train application. Experimental results evaluate the parameters of our two stages detection approach and show that our algorithm is robust in detecting and classifying railway workers for a real-time implementation on an embedded system. Our implementation on an embedded system allows a detection with a correct classification rate of 98.5 % of accuracy and a classification time of 1 ms per frame.Show less >
Show more >In this paper, we present a combined approach for human localization and classification in Autonomous Train application. Our contribution is threefold. (a) The creation of a new dataset for workers wearing orange vests in a railway environment context. (b) A deep learning supervised YOLO object detector for persons detection combined with a linear SVM (Support Vector Machine) classifier for persons classification into workers wearing orange vests or travelers. (c) A realtime vision-based technique for the environment monitoring in a driverless train application. Experimental results evaluate the parameters of our two stages detection approach and show that our algorithm is robust in detecting and classifying railway workers for a real-time implementation on an embedded system. Our implementation on an embedded system allows a detection with a correct classification rate of 98.5 % of accuracy and a classification time of 1 ms per frame.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Comment :
ISBN 978-1-7281-7575-1 e-ISBN 978-1-7281-7574-4
Source :