scholarly journals Real-World Marine Radar Datasets for Evaluating Target Tracking Methods

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4641
Author(s):  
Jaya Shradha Fowdur ◽  
Marcus Baum ◽  
Frank Heymann
Keyword(s):  
Target Tracking ◽  
Target Detection ◽  
Situation Awareness ◽  
Real World ◽  
Autonomous Navigation ◽  
Automatic Identification ◽  
Identification System ◽  
Initial Dataset ◽  
Marine Radar ◽  
German Aerospace

As autonomous navigation is being implemented in several areas including the maritime domain, the need for robust tracking is becoming more important for traffic situation awareness, assessment and monitoring. We present an online repository comprising three designated marine radar datasets from real-world measurement campaigns to be employed for target detection and tracking research purposes. The datasets have their respective reference positions on the basis of the Automatic Identification System (AIS). Together with the methods used for target detection and clustering, a novel baseline algorithm for an extended centroid-based multiple target tracking is introduced and explained. We compare the performance of our algorithm to its standard version on the datasets using the AIS references. The results obtained and some initial dataset specific analysis are presented. The datasets, under the German Aerospace Centre (DLR)’s terms and agreements, can be procured from the company website’s URL provided in the article.

scholarly journals Verification of the image processing system in real conditions

2021 ◽  
Vol 3 (1) ◽  
pp. 33-37
Author(s):  
Piotr Wołejsza ◽  
Jolanta Koszelew ◽  
Krzysztof Matuk ◽  
Oskar Świda
Keyword(s):  
Image Processing ◽  
Autonomous Navigation ◽  
Processing System ◽  
Automatic Identification ◽  
Identification System ◽  
Sources Of Information ◽  
Research Project ◽  
Image Processing System ◽  
Automatic Identification System ◽  
Autonomous Vessel

Autonomous Vessel with an Air Look, is a research project that aims to develop autonomous navigation of ships. The system uses three independent sources of information i.e. radar, AIS ? Automatic Identification System and cameras, which can be located on a drone or ship?s superstructure. The article presents the results of testing of an image processing system in real conditions on m/f Wolin.

scholarly journals A Hybrid Method for Inland Ship Recognition Using Marine Radar and Closed-Circuit Television

2021 ◽  
Vol 9 (11) ◽  
pp. 1199
Author(s):  
Xinglong Liu ◽  
Yicheng Li ◽  
Yong Wu ◽  
Zhiyuan Wang ◽  
Wei He ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Weather Conditions ◽  
Calibration Method ◽  
Closed Circuit ◽  
Automatic Identification ◽  
Identification System ◽  
Closed Circuit Television ◽  
Multi Scale ◽  
Marine Radar ◽  
Ship Recognition

Vessel recognition plays important role in ensuring navigation safety. However, existing methods are mainly based on a single sensor, such as automatic identification system (AIS), marine radar, closed-circuit television (CCTV), etc. To this end, this paper proposes a coarse-to-fine recognition method by fusing CCTV and marine radar, called multi-scale matching vessel recognition (MSM-VR). This method first proposes a novel calibration method that does not use any additional calibration target. The calibration is transformed to solve an N point registration model. Furthermore, marine radar image is used for coarse detection. A region of interest (ROI) area is computed for coarse detection results. Lastly, we design a novel convolutional neural network (CNN) called VesNet and transform the recognition into feature extraction. The VesNet is used to extract the vessel features. As a result, the MVM-VR method has been validated by using actual datasets collected along different waterways such as Nanjing waterway and Wuhan waterway, China, covering different times and weather conditions. Experimental results show that the MSM-VR method can adapt to different times, different weather conditions, and different waterways with good detection stability. The recognition accuracy is no less than 96%. Compared to other methods, the proposed method has high accuracy and great robustness.

scholarly journals Target Detection For Visual Collision Avoidance System

2021 ◽  
Vol 93 (7s) ◽  
pp. 159-166
Author(s):  
Miro Petković ◽  
◽  
Danko Kezić ◽  
Igor Vujović ◽  
Ivan Pavić ◽  
...  
Keyword(s):  
Object Detection ◽  
Target Tracking ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Automatic Identification ◽  
Surveillance Systems ◽  
International Regulations ◽  
Collision Avoidance System ◽  
Small Targets

Automatic Identification Systems (AIS) and Automatic Radar Plotting Aids (ARPA) are commonly used to detect targets for collision avoidance. However, AIS cannot detect targets without AIS transmitters and ARPA has limitations due to blind sector and small targets may not be detected. Advances in computer performance and video-based detection generated much interest in developing intelligent video surveillance systems to achieve autonomous navigation. To develop a reliable collision avoidance system, we propose the use of a visual camera for real-time object detection and target tracking. Moreover, the system should follow the International Regulations for Preventing Collisions at Sea (COLREGs) to avoid catastrophic accidents. In this paper only a part of the system is presented. For real-time object detection, the You Only Look Once (YOLO) ver. 3 convolutional neural network is used, and the target tracking filter based on a Kalman filter with built-in estimated relative position and velocity.

scholarly journals AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain

2019 ◽  
Vol 8 (1) ◽  
pp. 5 ◽  
Author(s):  
Azzeddine Bakdi ◽  
Ingrid Kristine Glad ◽  
Erik Vanem ◽  
Øystein Engelhardtsen
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Risk Identification ◽  
Automatic Identification ◽  
Identification System ◽  
Large Area ◽  
Continuous Growth ◽  
Country Level ◽  
Spatial Risk

The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy.

scholarly journals Evaluation of PNT Error Limits Using Real World Close Encounters from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Evelin Engler ◽  
Paweł Banyś ◽  
Hans-Georg Engler ◽  
Michael Baldauf ◽  
Frank Sill Torres
Keyword(s):  
Collision Avoidance ◽  
Real World ◽  
Simplified Model ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Data ◽  
Close Encounters ◽  
Data Provision ◽  
Erroneous Data ◽  
Better Than

Collision avoidance is one of the main tasks on board ships to ensure safety at sea. To comply with this requirement, the direct ship environment, which is often modelled as the ship’s domain, has to be kept free of other vessels and objects. This paper addresses the question to which extent inaccuracies in position (P), navigation (N), and timing (T) data impact the reliability of collision avoidance. Employing a simplified model of the ship domain, the determined error bounds are used to derive requirements for ship-side PNT data provision. For this purpose, vessel traffic data obtained in the western Baltic Sea based on the automatic identification system (AIS) is analysed to extract all close encounters between ships considered as real-world traffic situations with a potential risk of collision. This study assumes that in these situations, erroneous data can lead to an incorrect assessment of the situation with regard to existing collision risks. The size of the error determines whether collisions are detected, spatially incorrectly assigned, or not detected. Therefore, the non-recognition of collision risks ultimately determines the limits of tolerable errors in the PNT data. The results indicate that under certain conditions, the probability of non-recognition of existing collision risks can reach non-negligible values, e.g., more than 1%, even though position accuracies are better than 10 m.

scholarly journals Research on Active Intelligent Perception Technology of Vessel Situation Based on Multisensor Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ruixin Ma ◽  
Yong Yin ◽  
Zilong Li ◽  
Jing Chen ◽  
Kexin Bao
Keyword(s):  
Target Detection ◽  
Point Cloud ◽  
Automatic Identification ◽  
Identification System ◽  
Detection Accuracy ◽  
Point Cloud Data ◽  
Multisensor Fusion ◽  
Fusion Algorithm ◽  
Monitoring Method ◽  
Cloud Data

In this paper, we focus on the safety supervision of inland vessels. This paper especially aims at studying the vessel target detection and dynamic tracking algorithm based on computer vision and the target fusion algorithm based on multisensor. For the vessel video target detection and tracking, this paper analyzes the current widely used methods and theories. Additionally, facing the application scenarios and characteristics of inland vessels, a comprehensive vessel video target detection algorithm is proposed in this paper. It is combined with a three-frame difference method based on Canny edge detection and a background subtraction method based on mixed Gaussian background modeling. Besides, for the multisensor target fusion, the processing method of laser point cloud data and automatic identification system (AIS) data is analyzed in this paper. Based on the idea of fuzzy mathematics, this paper proposes a method for calculating the fuzzy correlation matrix with normal membership function, which realizes the fusion of vessel track features of laser point cloud data and AIS data under dynamic video correction. Finally, through this method, a set of vessel situation active intelligent perception systems based on multisensor fusion was developed. Experiments show that this method has better environmental applicability and detection accuracy than traditional manual detection and any single monitoring method.

scholarly journals Architecture for Trajectory-Based Fishing Ship Classification with AIS Data

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3782 ◽  
Author(s):  
David Sánchez Pedroche ◽  
Daniel Amigo ◽  
Jesús García ◽  
José Manuel Molina
Keyword(s):  
Real World ◽  
Binary Classification ◽  
Classification Problem ◽  
Automatic Identification ◽  
Identification System ◽  
Preparation Process ◽  
Data Preparation ◽  
Real World Data ◽  
Fishing Vessels ◽  
Ship Classification

This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information.

scholarly journals DATA QUALITY ASSESSMENT FOR MARITIME SITUATION AWARENESS

2015 ◽  
Vol II-3/W5 ◽  
pp. 291-296 ◽  
Author(s):  
C. Iphar ◽  
A. Napoli ◽  
C. Ray
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Situation Awareness ◽  
Traffic Management ◽  
Situational Awareness ◽  
Methodological Approach ◽  
Automatic Identification ◽  
Identification System ◽  
Data Errors ◽  
Data Quality Assessment

The Automatic Identification System (AIS) initially designed to ensure maritime security through continuous position reports has been progressively used for many extended objectives. In particular it supports a global monitoring of the maritime domain for various purposes like safety and security but also traffic management, logistics or protection of strategic areas, etc. In this monitoring, data errors, misuse, irregular behaviours at sea, malfeasance mechanisms and bad navigation practices have inevitably emerged either by inattentiveness or voluntary actions in order to circumvent, alter or exploit such a system in the interests of offenders. This paper introduces the AIS system and presents vulnerabilities and data quality assessment for decision making in maritime situational awareness cases. The principles of a novel methodological approach for modelling, analysing and detecting these data errors and falsification are introduced.

scholarly journals Comparison of Inland Ship Emission Results from a Real-World Test and an AIS-Based Model

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1611
Author(s):  
Han Jiang ◽  
Di Peng ◽  
Yunjing Wang ◽  
Mingliang Fu
Keyword(s):  
Test Data ◽  
Real World ◽  
Emission Inventory ◽  
Calculated Data ◽  
Automatic Identification ◽  
Identification System ◽  
Emission Model ◽  
Emission Rates ◽  
Inland River ◽  
Ship Emission

Inland shipping is pivotal to the comprehensive transport system of China. However, ship emission has become a major air polluter in inland river regions, and relevant emission inventories are urgently needed. Currently, the Automatic identification System based (AIS-based)emission model is widely used in calculating the ocean-going ship emission inventory. However, due to the lack of AIS data in the river area, the inland ship emission inventory mainly uses the fuel consumption method. With the continuous improvement of AIS data quality in the river area, the AIS-based emission model can be adopted in the development of inland ship emission inventory. However, there are few studies on the evaluation of the accuracy of the inland ship emissions using the AIS-based emission model. This study makes a comparison between test data and model-calculated data to evaluate the accuracy of the AIS-based emission models. Inland ship activities are divided into being at berth, maneuvering (port departure and port arrival), and on cruise modes in an AIS-based emission model. The model-calculated CO2, HC, and NOx emission rates can cover those onboard emission test data, but the values from the model are much lower. The total average ratios of test data to model-calculated data for CO2, CO, HC, and NOx are 2.66, 19.12, 2.46, and 3.16 when engine loads are below 60%. In upstream cruise mode, average emission rates of CO2, CO, HC, and NOx from the real-world test are 1.91–6.48, 8.78–27.83, 3.05–8.96, and 4.06–5.96 times higher than those from the AIS-based model, respectively. However, those are only 1.08–1.51, 6.74–9.67, 2.03–3.75, and 1.65–2.75 times higher than those from the AIS-based model in downstream cruise mode.

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