Object Tracking for an Autonomous Unmanned Surface Vehicle

The conventional algorithm used for target recognition and tracking suffers from the uncertainties of the environment, robot/sensors and object, such as variations in illumination and viewpoint, occlusion and seasonal change, etc. This paper proposes a deep-learning based surveillance and reconnaiss...

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Bibliographic Details
Published in:Machines (Basel) 2022-05, Vol.10 (5), p.378
Main Authors: Lee, Min-Fan Ricky, Lin, Chin-Yi
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Communication
Computer architecture
Crack propagation
Deep learning
Design
Global positioning systems
GPS
Machine learning
Methods
Neural networks
Object recognition
Object recognition (Computers)
object tracking
Occlusion
Pattern recognition
R&D
Research & development
Robots
surface robot
Surface vehicles
Surveillance
Target detection
Target recognition
Tracking
Tracking networks
Unmanned aerial vehicles
unmanned surface vehicle
Unmanned vehicles
Wireless networks
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Summary:The conventional algorithm used for target recognition and tracking suffers from the uncertainties of the environment, robot/sensors and object, such as variations in illumination and viewpoint, occlusion and seasonal change, etc. This paper proposes a deep-learning based surveillance and reconnaissance system for unmanned surface vehicles by adopting the Siamese network as the main neural network architecture to achieve target tracking. It aims to detect and track suspicious targets. The proposed system perceives the surrounding environment and avoids obstacles while tracking. The proposed system is evaluated with accuracy, precision, recall, P-R curve, and F1 score. The empirical results showed a robust target tracking for the unmanned surface vehicles. The proposed approach contributes to the intelligent management and control required by today’s ships, and also provides a new tracking network architecture for the unmanned surface vehicles.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines10050378