Titre :

A novel multi-view pedestrian detection database for collaborative intelligent transportation systems

Auteur(s) :

Ben Khalifa, Anouar [Auteur]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Alouani, Lihsen [Auteur]
INSA Institut National des Sciences Appliquées Hauts-de-France [INSA Hauts-De-France]
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]
Mahjoub, Mohamed Ali [Auteur]
Université de Sousse
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Rivenq, Atika [Auteur]
INSA Institut National des Sciences Appliquées Hauts-de-France [INSA Hauts-De-France]
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]

Titre de la revue :

Future Generation Computer Systems

Pagination :

506-527

Éditeur :

Elsevier

Date de publication :

2020-12

ISSN :

0167-739X

Mot(s)-clé(s) en anglais :

Multi-view
Environment perception
Collaborative intelligence
Pedestrian detection
Infrastructure to vehicle
CNN

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Recent advances in machine-learning, especially in deep neural networks have significantly accelerated the development and deployment of transport-oriented intelligent designs with increasingly high efficiency. While these ...
Lire la suite >Recent advances in machine-learning, especially in deep neural networks have significantly accelerated the development and deployment of transport-oriented intelligent designs with increasingly high efficiency. While these technologies are exceptionally promising toward revolutionizing our current mobility and reducing the number of road accidents, the way to safe Intelligent Transportation Systems (ITS) remains long. Since pedestrians are the most vulnerable road users, designing accurate pedestrian detection methods is a priority task. However, traditional monocular pedestrian detection methods are limited, especially in occlusion handling. Hence, a collaborative perception scheme in which vehicles no longer restrict their input data to their immediate embedded sensors and rather exploit data from remote sensors is necessary to achieve a more comprehensive environment perception. In this work, we propose a novel public dataset: Infrastructure to Vehicle Multi-View Pedestrian Detection Database (I2V-MVPD) that combines synchronized images from both a mobile camera embedded in a car and a static camera in the road infrastructure. We also propose a new multi-view pedestrian detection framework based on collaborative intelligence between vehicles and infrastructure. Our results show a significant improvement in detection performance over monocular detection.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL