Improving the Efficiency of 3D Monocular Object Detection and Tracking for Road and Railway Smart Mobility

Autor: Alexandre Evain, Antoine Mauri, François Garnier, Messmer Kounouho, Redouane Khemmar, Madjid Haddad, Rémi Boutteau, Sébastien Breteche, Sofiane Ahmedali

Publikováno v: Sensors, Vol 23, Iss 6, p 3197 (2023)

Three-dimensional (3D) real-time object detection and tracking is an important task in the case of autonomous vehicles and road and railway smart mobility, in order to allow them to analyze their environment for navigation and obstacle avoidance purp

Externí odkaz: https://doaj.org/article/38db0a7787014d76928b8ac9281be55b

Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility

Autor: Antoine Mauri, Redouane Khemmar, Benoit Decoux, Madjid Haddad, Rémi Boutteau

Publikováno v: Journal of Imaging, Vol 7, Iss 8, p 145 (2021)

For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making,

Externí odkaz: https://doaj.org/article/065d796043354852b6ef0247d72c2319

Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments

Autor: Safa Ouerghi, Rémi Boutteau, Xavier Savatier, Fethi Tlili

Publikováno v: Sensors, Vol 18, Iss 4, p 939 (2018)

In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of vis

Externí odkaz: https://doaj.org/article/df931bdfe27c41d7b9e0b08548fd26ca

PHROG: A Multimodal Feature for Place Recognition

Autor: Fabien Bonardi, Samia Ainouz, Rémi Boutteau, Yohan Dupuis, Xavier Savatier, Pascal Vasseur

Publikováno v: Sensors, Vol 17, Iss 5, p 1167 (2017)

Long-term place recognition in outdoor environments remains a challenge due to high appearance changes in the environment. The problem becomes even more difficult when the matching between two scenes has to be made with information coming from differ

Externí odkaz: https://doaj.org/article/05bfe723910c48338e87c9df7eb2556d

A Comparative Study of Deep Learning-based Depth Estimation Approaches: Application to Smart Mobility

Autor: Tahar Benmoumen, Redouane Khemmar, Benoit Decoux, Rémi Boutteau, Madjid Haddad, Antoine Mauri

Publikováno v: 8th International Conference on Smart Computing and Communications (ICSCC 2021)
8th International Conference on Smart Computing and Communications (ICSCC 2021), Jul 2021, Kochi, India

In autonomous vehicle systems, the quality of scene perception is of great importance for security preoccupation in road environments. In this context, an accurate localization of potential obstacles is one of the most challenging tasks. In recent ye

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f54b31c06b630c7d1169ab5c01a791b
https://doi.org/10.1109/icscc51209.2021.9528220