Small object tracking in infrared images is widely utilized in various fields, such as video surveillance, infrared guidance, and unmanned aerial vehicle monitoring. The existing small target detection strategies in infrared images suffer from submerging the target in heavy cluttered infrared (IR) maritime images. To overcome this issue, we use the original image and the corresponding encoded image to apply our model. We use the local directional number patterns algorithm to encode the original image to represent more unique details. Our model is able to learn more informative and unique features from the original and encoded image for visual tracking. In this study, we explore the best convolutional filters to obtain the best possible visual tracking results by finding those inactive to the backgrounds while active in the target region. To this end, the attention mechanism for the feature extracting framework is investigated comprising a scale-sensitive feature generation component and a discriminative feature generation module based on the gradients of regression and scoring losses. Comprehensive experiments have demonstrated that our pipeline obtains competitive results compared to recently published papers.

红外图像小目标跟踪广泛应用于视频监控、红外制导、无人机监控等各个领域。现有红外图像中的小目标检测策略存在将目标淹没在重杂波红外 (IR) 海事图像中的问题。为了克服这个问题，我们使用原始图像和相应的编码图像来应用我们的模型。我们使用局部方向数模式算法对原始图像进行编码以表示更多独特的细节。我们的模型能够从原始和编码图像中学习更多信息和独特的特征，以进行视觉跟踪。在这项研究中，我们探索了最好的卷积滤波器，通过在目标区域中找到那些对背景不活跃的滤波器来获得最佳的视觉跟踪结果。为此，研究了特征提取框架的注意机制，包括尺度敏感特征生成组件和基于回归梯度和评分损失的判别特征生成模块。综合实验表明，与最近发表的论文相比，我们的管道获得了具有竞争力的结果。