一种自适应红外目标尺寸变化的检测跟踪算法

在实际场景中随着红外探测距离的缩小，红外弱小目标的尺寸等会动态增长，常用的红外弱小目标检测跟踪算法便无法继续稳定检测与跟踪。为解决上述问题，本文提出了一种自适应红外目标尺寸变化的检测跟踪方法，借助低阈值信噪比实现弱小目标的初筛，并通过自适应尺寸分割避免大目标漏检误检，构建备选目标库，最后配合使用卡尔曼算法模型预测运动轨迹，完成小范围波门检测，实现目标跟踪。与传统DBT（Detection Before Track）跟踪检测算法相比，本文算法可同时兼顾弱小目标和大尺寸目标的检测跟踪，在所选目标尺寸动态增长的场景中，本文算法的检测跟踪率提升了约10%。

Adaptive Detection and Tracking Algorithm for Infrared Target Size Variation

In an actual scenario, as the detection distance decreases, the size of the infrared weak and small targets increases dynamically. Commonly used infrared weak and small target detection and tracking algorithms cannot continue to detect and track stably. To address these problems, we propose an adaptive infrared target size change detection and tracking method. The initial screening of weak and small targets is realized with the help of a low threshold signal-to-noise ratio and circumvents the missed detection and false detection of large targets via adaptive size segmentation. Subsequently, we built an alternative target library. Finally, the Kalman algorithm model was adopted to predict the motion trajectory, complete the small-scale wave-gate detection, and realize target tracking. Compared with the DBT conventional detection and tracking algorithm, our method considers the detection and tracking of weak and small targets and large-sized targets simultaneously. In the selected scene, where the target size dynamically increases, the detection and tracking rate of the algorithm in this study is improved by approximately 10%.