关键词: 多维特征融合机制, 尺度变化, 相关滤波机制, 多目标船舶跟踪, 智能船舶

Abstract:

Traditional visual ship tracking task mainly focuses on single target tracking, and thus less attention is paid to multi-ship tracking task. To address the issue, a novel ship tracking framework based on multi-dimension feature fusion mechanism and scale change estimation is proposed. The ship target position is firstly estimated with multi-dimension features by finding the maximum response between the training sample and the input maritime image, and its scale is then estimated with a scale filter. The experimental results show that average ship tracking error in different maritime traffic scenarios are less than 10 pixels, which verifies the effectiveness and reliability of the proposed framework.

Key words: multi-dimension feature fusion, scale variation, correlation filtering mechanism, multi-target ship tracking, smart ship