A High-precision Tracker for Small Objects in Intelligent Vessel Navigation Scenarios

Tracking small sea-surface objects is crucial for environmental perception in intelligent vessels. However, due to the limited feature information of small objects and the motion-induced instability of shipborne cameras, existing methods still face challenges in tracking accuracy and stability. To address these issues and enhance the performance of small sea-surface objects tracking, a novel tracker named SeaMicroTracker is developed. The tracker integrates deep learning-based object detection with an optimized object association algorithm to enhance robustness. Specifically, for object detection, an enhanced Convolutional Neural Network (eYOLOv5) model is employed to accurately extract the positional information of small sea-surface objects. For object association, an observation-centric Kalman filter (OKF) is designed to enhance state estimation reliability under shipborne camera motion. Additionally, a Manhattan distance-based intersection over union (MDIoU) metric is proposed to improve association accuracy in dynamic maritime environments. Furthermore, a progressive refinement cascade matching (PRCM) strategy is developed to improve the tracker's adaptability to complex maritime conditions and target occlusions, thereby further enhancing target association performance. Experimental results on the Jari maritime tracking dataset show that SeaMicroTracker achieves a multiple object tracking accuracy (MOTA) of 80.6 and an identification F1 score (IDF1) of 64.0, demonstrating significant advantages in tracking accuracy and stability. Compared with the baseline method ByteTrack, the proposed tracker improves MOTA and IDF1 by 27.9% and 34.7%, respectively, while effectively reducing ID-switching events. Moreover, the tracker achieves an average tracking speed of 30.1 FPS, satisfying real-time requirements for engineering applications.