Low Altitude Georeferencing for Imaging Sensors in Maritime Tracking

Helgesen, Øystein Kaarstad; Brekke, Edmund; Stahl, Annette; Engelhardtsen, Øystein

doi:10.1016/j.ifacol.2020.12.1449

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Our georeferencing method offers several improvements when compared with the state of the art in ship georeferencing from static oblique view images [ 29 ]. Firstly, our methodology can be replicated using any existing static camera at a maritime infrastructure without previous knowledge about the camera, such as location, elevation, field of view and tilt angle.…”

Section: Discussionmentioning

confidence: 99%

“…In the field of ship georeferencing, existing works made use of radar [ 26 ], remote sensing using synthetic aperture radar [ 27 ] and AIS [ 28 ]. Helgesen et al, in [ 29 ], proposed a pipeline for ship detection and georeferencing using their private dataset with oblique view images of the water. They use the pinhole camera calibration transformation matrix proposed in [ 30 ] to georeference bounding boxes of detected ships.…”

Section: Introductionmentioning

confidence: 99%

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Ship Segmentation and Georeferencing from Static Oblique View Images

Carrillo-Perez¹,

Barnes²,

Stephan³

2022

Sensors

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Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22±10) m for ships detected within a 400 m range, and (53±24) m for ships over 400 m away from the camera.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ship Segmentation and Georeferencing from Static Oblique View Images

Carrillo-Perez¹,

Barnes²,

Stephan³

2022

Sensors

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“…where R c is the measurement noise in pixel coordinates and J is the Jacobian of the pixel-to-Cartesian conversion for the measurement as done in [20]. Deriving an analytic expression of the partial derivatives of the reverse pinhole equations ( 14) is possible, however, the resulting expression is long and numeric calculation might therefore be more attractive.…”

Section: ) Distribution Linearizationmentioning

confidence: 99%

“…In this work, we propose a multi-camera detection pipeline that circumvents these issues. Adapting the georeferencing method presented in [20] to a moving platform allows the usage of implicit information to estimate target ranges from image detections, eliminating the need for bearingsonly tracking or complex monocular or stereo-based depth estimation. Clustering-based sensor fusion is then used to perform measurement-level sensor fusion of the different cameras, yielding only a single measurement from each target.…”

Section: Introductionmentioning

confidence: 99%

Maritime Tracking With Georeferenced Multi-Camera Fusion

2023

Self Cite

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Cameras form an essential part of any autonomous surface vehicle's sensor package, both for COLREGs compliance to detect light signals and for identifying and tracking other vessels. Due to limited fields of view compared to more traditional autonomy sensors such as lidars and radars, an autonomous surface vessel will typically be equipped with multiple cameras which can induce biases when used in tracking if a target is present in multiple image frames. In this work, we propose a novel pipeline for camera-based maritime tracking that combines georeferencing with clustering-based multi-camera fusion for bias-free camera measurements with target range estimates. Using real-world datasets collected using the milliAmpere research platform the performance of this pipeline exceeded a lidar benchmark across multiple performance measures, both in pure detection performance and as part of a JIPDA-based tracking system.

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