[1]LI Xiang,ZHANG Ting,LIU Zhaoying,et al.Infrared ship target tracking based on saliency guided siamese network[J].CAAI Transactions on Intelligent Systems,2024,19(6):1428-1437.[doi:10.11992/tis.202306004]
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Infrared ship target tracking based on saliency guided siamese network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 6 Page number: 1428-1437 Column: 学术论文—机器感知与模式识别 Public date: 2024-12-05
Title:
Infrared ship target tracking based on saliency guided siamese network
Author(s):
LI Xiang1;  ZHANG Ting1;  LIU Zhaoying1;  LIU Bo1;  LI Yujian2
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
Keywords:
infrared ship tracking; siamese network; salient object detection; feature fusion; shared correlation; multi-task learning; convolutional neural network; deep learning
CLC:
TP391
DOI:
10.11992/tis.202306004
Abstract:
Infrared images often have features with low discriminative power, making it difficult to segment targets from the background using existing methods. Owing to the nature of infrared imaging, targets usually exhibit high local saliency. To address this, we propose a method for tracking infrared ship targets using saliency-guided Siamese networks (SGSiam). This approach uses the target’s saliency as prior knowledge to guide the tracking model for precise target localization. First, this study presents a saliency prediction network and a saliency enhancement network. The saliency prediction network generates a global saliency map of the search area, which is input into the saliency enhancement network to strengthen the target and improve the discriminative ability of the model. Second, a shared cross-correlation architecture is designed to calculate the similarity between the template image features and the saliency-enhanced search region features, thus improving the model efficiency and performance through shared feature maps for classification and regression tasks. Finally, owing to the lack of publicly available infrared ship tracking data sets, we introduce a new infrared ship data set (ISD), which includes 16 different ship types and 7800 video frames with manual annotations. Experimental results on ISD show that our model outperforms 18 commonly used tracking models, achieving the highest accuracy and the highest expected average overlap score.
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