LIDAR Extended Object Tracking of a Maritime Vessel Using an Ellipsoidal Contour Model

Ruud, Kristian Amundsen; Brekke, Edmund; Eidsvik, Jo

doi:10.1109/sdf.2018.8547047

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…The traditional vessel tracking system is usually implemented by satellite tracking technology [33,34]. The satellite tracking technology needs to have a GPS receiver on the vessel to receive the signal from the satellite and return the signal to the ground control center [35], which has the advantage of wide coverage but the signal may be interfered with.…”

Section: Vessel Trackingmentioning

confidence: 99%

A Real-Time Vessel Detection and Tracking System Based on LiDAR

Qi,

Huang,

Zhang

et al. 2023

Sensors

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Vessel detection and tracking is of utmost importance to river traffic. Efficient detection and tracking technology offer an effective solution to address challenges related to river traffic safety and congestion. Traditional image-based object detection and tracking algorithms encounter issues such as target ID switching, difficulties in feature extraction, reduced robustness due to occlusion, target overlap, and changes in brightness and contrast. To detect and track vessels more accurately, a vessel detection and tracking algorithm based on the LiDAR point cloud was proposed. For vessel detection, statistical filtering algorithms were integrated into the Euclidean clustering algorithm to mitigate the effect of ripples on vessel detection. Our detection accuracy of vessels improved by 3.3% to 8.3% compared to three conventional algorithms. For vessel tracking, L-shape fitting of detected vessels can improve the efficiency of tracking, and a simple and efficient tracking algorithm is presented. By comparing three traditional tracking algorithms, an improvement in multiple object tracking accuracy (MOTA) and a reduction in ID switch times and number of missed detections were achieved. The results demonstrate that LiDAR point cloud-based vessel detection can significantly enhance the accuracy of vessel detection and tracking.

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Section: Vessel Trackingmentioning

confidence: 99%

A Real-Time Vessel Detection and Tracking System Based on LiDAR

Qi,

Huang,

Zhang

et al. 2023

Sensors

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“…Detailed reviews on extent approximation methods can be found in [5,14,15]. Ellipses have so far been the most favored basic shape to approximate vessels' extents in maritime applications, where we represent the length and width by means of the ellipse's axes and the true heading by the ellipse's orientation [16][17][18][19]. Traditional methods for elliptical target tracking are based on the Random Matrix Method (RMM) framework, where a target's kinematic state is modeled as a Gaussian distribution and its extent by a Wishart distribution, which is represented using an Symmetric and Positive Definite (SPD) matrix [6,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

An Overview of the PAKF-JPDA Approach for Elliptical Multiple Extended Target Tracking Using High-Resolution Marine Radar Data

et al. 2023

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Ground radar stations observing specific regions of interest nowadays provide detections in the form of point-clouds. This article focuses on a framework that consists of an elliptical multitarget tracker, referred to as Principal-Axes based Kalman Filter (PAKF)-based Joint Probabilistic Data Association (JPDA) (PAKF-JPDA), to enable maritime traffic monitoring. The framework touches on two major stages, target detection and target tracking. For the former, we employed a clustering approach and for the latter, we presented a data-association-based version of the PAKF tracker with an automatic track management functionality. The framework’s benefits are demonstrated when it is applied to the radar streaming in a harbor setting based on a homogeneous multisensor tracking system by comparing our results against their corresponding reference data with visualizations, including performance measures.

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“…A method for dealing with obstacle detection in LiDAR and cameras is proposed in (Faggioni et al, 2022).Figure 1 shows a possible application of the discussed procedure in a multi-sensor data fusion perceptive system. In paritcular, obstacles are detected in the LiDAR point cloud and the acquired targets are tracked by adopting a low computational customized method, instead of the probabilistic approaches generally employed (Ruud et al, 2018) (Lee et al, 2010). To simplify the handling of the problem, tracking is performed on the centroids of the point clouds; such an approach is often adopted to decrease the computational cost (Fang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Detecting and Tracking Multi-Object in Real Marine Environment

Martelli¹,

Faggioni²,

Ponzini³

2022

Preprint

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In recent years, the maritime sector has been witnessing a growing interest in autonomous navigation due to its advantages in safety. Within this framework, it is challenging to have available an automatic collision avoidance system based on data coming from the onboard sensors. For instance, LiDAR (Light Detection and Ranging) sensors are often employed to obtain a virtualized model of the surrounding environment in order to obtain an increase of situational awareness. Deep Learning approaches are widely used in the automotive sector for obstacle detection, particularly, trained Convolutional Neural Networks. Still, they imply a training phase based on a large dataset of pre-labelled LiDAR scans. Unfortunately, an extensive training dataset is not yet available for the marine environment. For such a reason, this paper presents in detail a low-computational alternative procedure based on Unsupervised Learning which overcomes the lack of a training dataset; each step is investigated, from the data sampling up to the multi-target tracking of the detected obstacles. In particular, the proposed object detection framework has been tested by means of an extensive on-field data collection campaign carried out during sea trials by equipping a ship with a LiDAR. Furthermore, Euclidean distance-based clustering algorithm and a bounding-box construction method based on Principal Component Analysis have been adopted. Moreover, a specific tracking system with no prediction filters is proposed to fulfil the strict time constraint for fast reactions in complex scenarios where several objects need to be tracked. The algorithm has been tested versus the well-known Global Nearest Neighbour tracker; such a comparison includes the computational cost and the results' accuracy. The whole approach has been tested on such a challenging and dynamic marine scenario, and the results obtained are presented and discussed. Such and outcome shows that the proposed approach can detect and track multiple objects with reasonable accuracy; moreover, the outputs are provided near real-time. To conclude, the pros, the weaknesses, and future developments are reported.

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LIDAR Extended Object Tracking of a Maritime Vessel Using an Ellipsoidal Contour Model

Cited by 17 publications

References 22 publications

A Real-Time Vessel Detection and Tracking System Based on LiDAR

A Real-Time Vessel Detection and Tracking System Based on LiDAR

An Overview of the PAKF-JPDA Approach for Elliptical Multiple Extended Target Tracking Using High-Resolution Marine Radar Data

Detecting and Tracking Multi-Object in Real Marine Environment

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