A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data
In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/14/6400 |
| _version_ | 1827731848383430656 |
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| author | Md Asif Bin Syed Imtiaz Ahmed |
| author_facet | Md Asif Bin Syed Imtiaz Ahmed |
| author_sort | Md Asif Bin Syed |
| collection | DOAJ |
| description | In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations comprising the geological and motion parameters of the vessels and associate them with respective vessels. The spatial and temporal variations inherent in these sequential observations make the association task challenging for traditional multi-object tracking algorithms. Additionally, the presence of overlapping tracks and missing data can further complicate the trajectory tracking process. To address these challenges, in this study, we approach this tracking task as a multivariate time series problem and introduce a 1D CNN-LSTM architecture-based framework for track association. This special neural network architecture can capture the spatial patterns as well as the long-term temporal relations that exist among the sequential observations. During the training process, it learns and builds the trajectory for each of these underlying vessels. Once trained, the proposed framework takes the marine vessel’s location and motion data collected through the automatic identification system (AIS) as input and returns the most likely vessel track as output in real-time. To evaluate the performance of our approach, we utilize an AIS dataset containing observations from 327 vessels traveling in a specific geographic region. We measure the performance of our proposed framework using standard performance metrics such as accuracy, precision, recall, and F1 score. When compared with other competitive neural network architectures, our approach demonstrates a superior tracking performance. |
| first_indexed | 2024-03-11T00:40:27Z |
| format | Article |
| id | doaj.art-d515b89325db4b4ead580076707ecfe8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:40:27Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d515b89325db4b4ead580076707ecfe82023-11-18T21:17:12ZengMDPI AGSensors1424-82202023-07-012314640010.3390/s23146400A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) DataMd Asif Bin Syed0Imtiaz Ahmed1Industrial and Management Systems Engineering Department, West Virginia University, Morgantown, WV 26506, USAIndustrial and Management Systems Engineering Department, West Virginia University, Morgantown, WV 26506, USAIn marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations comprising the geological and motion parameters of the vessels and associate them with respective vessels. The spatial and temporal variations inherent in these sequential observations make the association task challenging for traditional multi-object tracking algorithms. Additionally, the presence of overlapping tracks and missing data can further complicate the trajectory tracking process. To address these challenges, in this study, we approach this tracking task as a multivariate time series problem and introduce a 1D CNN-LSTM architecture-based framework for track association. This special neural network architecture can capture the spatial patterns as well as the long-term temporal relations that exist among the sequential observations. During the training process, it learns and builds the trajectory for each of these underlying vessels. Once trained, the proposed framework takes the marine vessel’s location and motion data collected through the automatic identification system (AIS) as input and returns the most likely vessel track as output in real-time. To evaluate the performance of our approach, we utilize an AIS dataset containing observations from 327 vessels traveling in a specific geographic region. We measure the performance of our proposed framework using standard performance metrics such as accuracy, precision, recall, and F1 score. When compared with other competitive neural network architectures, our approach demonstrates a superior tracking performance.https://www.mdpi.com/1424-8220/23/14/6400Maritime Track Associationneural networksdeep learningautomatic identification system (AIS)multi-object tracking |
| spellingShingle | Md Asif Bin Syed Imtiaz Ahmed A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data Sensors Maritime Track Association neural networks deep learning automatic identification system (AIS) multi-object tracking |
| title | A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data |
| title_full | A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data |
| title_fullStr | A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data |
| title_full_unstemmed | A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data |
| title_short | A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data |
| title_sort | cnn lstm architecture for marine vessel track association using automatic identification system ais data |
| topic | Maritime Track Association neural networks deep learning automatic identification system (AIS) multi-object tracking |
| url | https://www.mdpi.com/1424-8220/23/14/6400 |
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