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Multi-Sensors System and Deep Learning Models for Object Tracking
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DOI：10.3390/s23187804.png)
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摘要
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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.
Keyword:
multi-sensors system
tracking
recurrent neural networks
sensor fusion
metric learning
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3.5
论文数:
7.2W
被引数:
20.9W
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