MOMT: A Maritime Real-Time Visual Multiobject Tracking Algorithm Based on Unmanned Surface Vehicles

Visual multiobject tracking (MOT) on the maritime surface based on unmanned surface vessels (USVs) is a crucial technology for realizing intelligent maritime surveillance. However, videos obtained from the maritime surface often encounter challenges such as scene blurring, oscillations of USV observ...

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Veröffentlicht in:IEEE sensors journal 2024-11, Vol.24 (21), p.35429-35447
Hauptverfasser: Niu, Longhui, Fan, Yunsheng, Liu, Ting, Han, Qi
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Sprache:eng
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Zusammenfassung:Visual multiobject tracking (MOT) on the maritime surface based on unmanned surface vessels (USVs) is a crucial technology for realizing intelligent maritime surveillance. However, videos obtained from the maritime surface often encounter challenges such as scene blurring, oscillations of USV observation platforms, and nonlinear movements of objects, significantly affecting the tracking performance. In response to these challenges, this article builds upon ByteTrack to introduce the maritime object motion tracker (MOMT), employing YOLOv7 as the object detector. Diverging from ByteTrack, MOMT proposes a new metric function and incorporates Gaussian cascade matching for a more refined handling of low-score and unmatched bounding boxes. In addition, an augmented observation Kalman filter (KF) module is developed to diminish the effects of nonlinear motion in objects. Specifically, first, in establishing the relationship between adjacent frames, the Fuse-distance metric function is proposed by synthesizing information on intersection over union (IoU) distance, object category, and size, to determine the optimal matching between detection boxes and KF prediction boxes. Second, the introduction of the Gaussian cascade matching module better addresses the issue of frequent identity switches (IDSs) caused by significant shaking of the USV observation platform. In addition, to cope with the impact of nonlinear object motion, the combination of camera motion compensation (CMC) and the observation-centric KF is employed to mitigate estimation errors in the KF. Finally, MOMT is compared with other state-of-the-art tracking algorithms on the Singapore Maritime Dataset, achieving competitive performance with 46.0 multiple object tracking accuracy (MOTA) and 66.7 IDF1, and operating in real time at a speed of 44.6 frames per second (FPS). The experimental results demonstrate that MOMT can achieve real-time and effective MOT in complex maritime scenarios, providing technical support for intelligent maritime surface monitoring.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3455572