Tracking Objects Using PHD Filter for USV Autonomous Capabilities

Oren, Gal; Zeitouni, Eran

doi:10.1007/978-3-642-33084-1_1

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…For sonar, Heidarsson and Sukhatme [21] processed the collected sonar data, followed by the processing and feature extraction of the overhead imagery, and then used a binary classification framework to detect marine obstacles. For lidar, Gal and Zeitouni, [22] used probability density estimation and Bayesian filters to identify and track sea-surface objects. Navigation radar, sonar, and lidar are commonly used detection sensors for autonomous ships.…”

Section: Comparison Of Sensors Used In the Maritime Environment For O...mentioning

confidence: 99%

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Lyu

Shao

Cheng

et al. 2023

IEEE Intell. Transport. Syst. Mag.

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Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The article also proposes the direction of future research for sea-surface object detection based on EO sensors.

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Section: Comparison Of Sensors Used In the Maritime Environment For O...mentioning

confidence: 99%

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Lyu

Shao

Cheng

et al. 2023

IEEE Intell. Transport. Syst. Mag.

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“…The algorithm performances were tested in various scenarios with real-time USV's video streams, and Gal states that the algorithm can be used for real-time applications with high success rate and fast time computation. Later, Gal and Zeitouni (2012) presented a multitarget automatic algorithm stages to acquire, identify, and track targets from a USV located in marine environments with LIDAR sensor challenging clutter. • A final problem that also needs attention is the one of route planning.…”

Section: Potential Future Research Directionsmentioning

confidence: 99%

Rigid wing sailboats: A state of the art survey

Silva

Friebe²,

Malheiro

et al. 2019

Ocean Engineering

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“…It was noticed that most of the obstacles appear clearly in the returned data while the sea surface does not provide any return. On the other hand, problems are posed by the presence of salt spray, winds, waves, currents and tides that produce image blurring and vibrations (Gal and Zeitouni, 2013). 3.…”

mentioning

confidence: 99%

Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

et al. 2017

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The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.

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Tracking Objects Using PHD Filter for USV Autonomous Capabilities

Cited by 6 publications

References 5 publications

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Rigid wing sailboats: A state of the art survey

Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

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