Boundary Attention Guided Sparse Feature Learning for Underwater Object Tracking in Edge Computing

Abstract

Underwater visual object tracking is crucial for marine resource exploration and military security. However, due to the effect of insufficient light and turbid background in underwater scenes, efficient and accurate target tracking cannot be realized on underwater edge devices with limited computing resources. To address this problem, we design an underwater object tracking network, namely DBSF, for edge computing devices based on sparse confidence feature learning guided by differential boundary attention. Specifically, we propose a differential boundary attention distribution model to compute the object edge distribution state to enhance the accurate perception of the underwater object edge structure. Then, the differential boundary attention-guided object tracking network learns to perceive the highly discriminative sparse features on the object structure, and computes the object sparse confidence matrix, which reduces the constraints of the edge devices with limited computational resources and ensures the tracking performance. Extensive experiments demonstrate that the DBSF network achieves accurate underwater target recognition and outperforms related advanced methods.