Multi-Sensors System and Deep Learning Models for Object Tracking

Autor:	Ghina El Natour, Guillaume Bresson, Remi Trichet
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	multi-sensors system tracking recurrent neural networks sensor fusion metric learning Chemical technology TP1-1185
Zdroj:	Sensors, Vol 23, Iss 18, p 7804 (2023)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s23187804
Popis:	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.
Databáze:	Directory of Open Access Journals
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