Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments
Vision-based target tracking is crucial for unmanned surface vehicles (USVs) to perform tasks such as inspection, monitoring, and surveillance. However, real-time tracking in complex maritime environments is challenging due to dynamic camera movement, low visibility, and scale variation. Typically,...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10848073/ |
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| author | Muhayy Ud Din Ahsan Baidar Bakht Waseem Akram Yihao Dong Lakmal Seneviratne Irfan Hussain |
| author_facet | Muhayy Ud Din Ahsan Baidar Bakht Waseem Akram Yihao Dong Lakmal Seneviratne Irfan Hussain |
| author_sort | Muhayy Ud Din |
| collection | DOAJ |
| description | Vision-based target tracking is crucial for unmanned surface vehicles (USVs) to perform tasks such as inspection, monitoring, and surveillance. However, real-time tracking in complex maritime environments is challenging due to dynamic camera movement, low visibility, and scale variation. Typically, object detection methods combined with filtering techniques are commonly used for tracking, but they often lack robustness, particularly in the presence of camera motion and missed detections. Although advanced tracking methods have been proposed recently, their application in maritime scenarios is limited. To address this gap, this study proposes a vision-guided object tracking framework for USVs, integrating state-of-the-art tracking algorithms with low-level control systems to enable precise tracking in dynamic maritime environments. We benchmarked the performance of seven distinct trackers, developed using advanced deep learning techniques such as Siamese Networks and Transformers, by evaluating them on both simulated and real-world maritime datasets. In addition, we evaluated the robustness of various control algorithms in conjunction with these tracking systems. The proposed framework was validated through simulations and real-world sea experiments, demonstrating its effectiveness in handling dynamic maritime conditions. The results show that SeqTrack, a Transformer-based tracker, performed best in adverse conditions, such as dust storms. Among the control algorithms evaluated, the linear quadratic regulator controller (LQR) demonstrated the most robust and smooth control, allowing for stable tracking of the USV. Videos and code can be found here: <uri>https://muhayyuddin.github.io/tracking/</uri>. |
| format | Article |
| id | doaj-art-fa50fc60829548cfbe9484610cb5a804 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-fa50fc60829548cfbe9484610cb5a8042025-01-28T00:01:22ZengIEEEIEEE Access2169-35362025-01-0113150141502710.1109/ACCESS.2025.353229910848073Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime EnvironmentsMuhayy Ud Din0https://orcid.org/0000-0001-6214-1077Ahsan Baidar Bakht1Waseem Akram2https://orcid.org/0000-0002-7401-5120Yihao Dong3https://orcid.org/0009-0002-8329-1810Lakmal Seneviratne4https://orcid.org/0000-0001-6405-8402Irfan Hussain5https://orcid.org/0000-0003-2759-0306Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, United Arab EmiratesKhalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, United Arab EmiratesKhalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, United Arab EmiratesKhalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, United Arab EmiratesKhalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, United Arab EmiratesKhalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, United Arab EmiratesVision-based target tracking is crucial for unmanned surface vehicles (USVs) to perform tasks such as inspection, monitoring, and surveillance. However, real-time tracking in complex maritime environments is challenging due to dynamic camera movement, low visibility, and scale variation. Typically, object detection methods combined with filtering techniques are commonly used for tracking, but they often lack robustness, particularly in the presence of camera motion and missed detections. Although advanced tracking methods have been proposed recently, their application in maritime scenarios is limited. To address this gap, this study proposes a vision-guided object tracking framework for USVs, integrating state-of-the-art tracking algorithms with low-level control systems to enable precise tracking in dynamic maritime environments. We benchmarked the performance of seven distinct trackers, developed using advanced deep learning techniques such as Siamese Networks and Transformers, by evaluating them on both simulated and real-world maritime datasets. In addition, we evaluated the robustness of various control algorithms in conjunction with these tracking systems. The proposed framework was validated through simulations and real-world sea experiments, demonstrating its effectiveness in handling dynamic maritime conditions. The results show that SeqTrack, a Transformer-based tracker, performed best in adverse conditions, such as dust storms. Among the control algorithms evaluated, the linear quadratic regulator controller (LQR) demonstrated the most robust and smooth control, allowing for stable tracking of the USV. Videos and code can be found here: <uri>https://muhayyuddin.github.io/tracking/</uri>.https://ieeexplore.ieee.org/document/10848073/USV navigationvision-based trackingvisual servoingmarine robotics |
| spellingShingle | Muhayy Ud Din Ahsan Baidar Bakht Waseem Akram Yihao Dong Lakmal Seneviratne Irfan Hussain Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments IEEE Access USV navigation vision-based tracking visual servoing marine robotics |
| title | Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments |
| title_full | Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments |
| title_fullStr | Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments |
| title_full_unstemmed | Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments |
| title_short | Benchmarking Vision-Based Object Tracking for USVs in Complex Maritime Environments |
| title_sort | benchmarking vision based object tracking for usvs in complex maritime environments |
| topic | USV navigation vision-based tracking visual servoing marine robotics |
| url | https://ieeexplore.ieee.org/document/10848073/ |
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