Multi-Sensors System and Deep Learning Models for Object Tracking
Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/18/7804 |
| _version_ | 1797577025384349696 |
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| author | Ghina El Natour Guillaume Bresson Remi Trichet |
| author_facet | Ghina El Natour Guillaume Bresson Remi Trichet |
| author_sort | Ghina El Natour |
| collection | DOAJ |
| description | Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects’ trajectories. Three deep recurrent network architectures were defined to achieve this, fine-tuning their weights to optimize the tracking process. The effectiveness of this proposed pipeline has been assessed, with diverse tracking scenarios demonstrated in both sub-urban and highway environments. The evaluations have yielded promising results, affirming the potential of this approach in enhancing autonomous navigation capabilities. |
| first_indexed | 2024-03-10T22:02:05Z |
| format | Article |
| id | doaj.art-296a451ae6fa4d31b1634932e59bab56 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:02:05Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-296a451ae6fa4d31b1634932e59bab562023-11-19T12:54:26ZengMDPI AGSensors1424-82202023-09-012318780410.3390/s23187804Multi-Sensors System and Deep Learning Models for Object TrackingGhina El Natour0Guillaume Bresson1Remi Trichet2Continental, 1 Av. Paul Ourliac, 31100 Toulouse, FranceVedecom, 23 bis All. des Marronniers, 78000 Versailles, FranceContinental, 1 Av. Paul Ourliac, 31100 Toulouse, FranceAutonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects’ trajectories. Three deep recurrent network architectures were defined to achieve this, fine-tuning their weights to optimize the tracking process. The effectiveness of this proposed pipeline has been assessed, with diverse tracking scenarios demonstrated in both sub-urban and highway environments. The evaluations have yielded promising results, affirming the potential of this approach in enhancing autonomous navigation capabilities.https://www.mdpi.com/1424-8220/23/18/7804multi-sensors systemtrackingrecurrent neural networkssensor fusionmetric learning |
| spellingShingle | Ghina El Natour Guillaume Bresson Remi Trichet Multi-Sensors System and Deep Learning Models for Object Tracking Sensors multi-sensors system tracking recurrent neural networks sensor fusion metric learning |
| title | Multi-Sensors System and Deep Learning Models for Object Tracking |
| title_full | Multi-Sensors System and Deep Learning Models for Object Tracking |
| title_fullStr | Multi-Sensors System and Deep Learning Models for Object Tracking |
| title_full_unstemmed | Multi-Sensors System and Deep Learning Models for Object Tracking |
| title_short | Multi-Sensors System and Deep Learning Models for Object Tracking |
| title_sort | multi sensors system and deep learning models for object tracking |
| topic | multi-sensors system tracking recurrent neural networks sensor fusion metric learning |
| url | https://www.mdpi.com/1424-8220/23/18/7804 |
| work_keys_str_mv | AT ghinaelnatour multisensorssystemanddeeplearningmodelsforobjecttracking AT guillaumebresson multisensorssystemanddeeplearningmodelsforobjecttracking AT remitrichet multisensorssystemanddeeplearningmodelsforobjecttracking |