Rémi Boutteau
9
Documents
Identifiants chercheurs
- boutteau
- 0000-0003-1078-5043
- Google Scholar : https://scholar.google.fr/citations?user=U-SrcPkAAAAJ&hl=fr
- IdRef : 150614314
Présentation
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<strong>Rémi Boutteau</strong> received his engineering degree from the <a href="http://imt-lille-douai.fr/en/" target="_blank">IMT Lille Douai</a> and his MSc degree in Computer Science from the <a href="https://www.univ-lille.fr/home/" target="_blank">University of Lille</a> in 2006. In 2010, he received his PhD degree from the <a href="http://rouenuniversity.univ-rouen.fr/" target="_blank">University of Rouen Normandy</a> for works related to Computer Vision (catadioptric sensors, 3D reconstruction, Structure-from-Motion). From 2009 to 2020, he was an Associate Professor at the <a href="https://www.esigelec.fr/en" target="_blank">ESIGELEC </a> engineering school and a researcher in the <a href="https://www.esigelec.fr/fr/irseem" target="_blank">IRSEEM</a> research institute. In 2018, he obtained the HDR (French Habilitation to supervise research) from the <a href="http://rouenuniversity.univ-rouen.fr/" target="_blank">University of Rouen Normandy</a> for his research on autonomous vehicles localization. Since 2020, he is a Full Professor at <a href="http://rouenuniversity.univ-rouen.fr/" target="_blank">University of Rouen Normandy</a> within the <a href="https://www.litislab.fr/equipe/index/sti" target="_blank">STI team</a> (Intelligent Transportation System) at the <a href="https://www.litislab.fr/accueil" target="_blank">LITIS Lab</a> (IT Laboratory, Information Processing and Systems). His research interests are perception, localization and computer vision dedicated to autonomous vehicles.
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STAF: Spatio-Temporal Attention Framework for Understanding Road Agents BehaviorsIEEE Access, 2022, 10, pp.55794-55804. ⟨10.1109/ACCESS.2022.3176861⟩
Article dans une revue
hal-04097562v1
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Recent Advances in Vision-Based On-Road Behaviors Understanding: A Critical SurveySensors, 2022, 22 (7), pp.2654. ⟨10.3390/s22072654⟩
Article dans une revue
hal-03630603v1
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Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart MobilityJournal of Imaging, 2021, 7 (8), pp.145. ⟨10.3390/jimaging7080145⟩
Article dans une revue
hal-03324039v1
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A Comparative Study of Deep Learning-based Depth Estimation Approaches: Application to Smart Mobility8th International Conference on Smart Computing and Communications (ICSCC 2021), Jul 2021, Kochi, India
Communication dans un congrès
hal-03277346v1
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Environment Perception-based Deep Learning. Application to Road and Railway Smart Mobility.V-ASET2021. 5th Edition of Applied Science, Engineering and Technology Virtual., Dec 2021, Teams, United Kingdom
Communication dans un congrès
hal-03525808v1
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Une nouvelle approche pour l'évaluation des méthodes monoculaires d'estimation de la profondeur basées sur l'apprentissage profondORASIS 2021, Centre National de la Recherche Scientifique [CNRS], Sep 2021, Saint Ferréol, France
Communication dans un congrès
hal-03339671v1
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A new Evaluation Approach for Deep Learning-based Monocular Depth Estimation MethodsThe 23rd IEEE International Conference on Intelligent Transportation Systems, Sep 2020, Rhodes (virtual conference), Greece
Communication dans un congrès
hal-02978149v1
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Toward Comprehensive Road Agents Behavior UnderstandingCSTI 2020 : 1er Colloque Francophone des Systèmes de Transports Intelligents, Nov 2020, Tunis, Tunisia
Communication dans un congrès
hal-03016779v1
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VIKINGS: An Autonomous Inspection Robot for the ARGOS ChallengeIEEE International Conference on Robotics and Automation (ICRA), May 2019, Montreal, Canada
Communication dans un congrès
hal-02143413v1
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