基于元学习的红外弱小点状目标跟踪算法

Infrared small target tracking algorithm based on meta-learning

为了在研究红外弱小点状目标的特征基础上有效解决训练数据不足的问题,采用了基于改进的元学习红外点状目标跟踪算法。首先将元学习通过预训练跟踪模型运用到卷积神经网络中,采用离线训练的方式在静态红外图像数据集上训练得到目标的通用表示,再通过在线训练的方式利用初始帧的目标位置学习得到目标的特定表示;通过卡尔曼滤波算法预测目标运动模型,得到最优的搜索区域。此外,为了解决遮挡造成的目标丢失问题,研究了重检测机制,并进行了理论分析和实验验证,取得了较好的跟踪结果,跟踪精度达到了90%。结果表明,该方法在同一数据集下相对其它跟踪算法实现了更精确地跟踪红外弱小点状目标的效果。该研究为机器学习算法在红外弱小点状目标跟踪中的应用提供了参考。

In order to effectively solve the problem of insufficient training data base on studying the characteristics of infrared dim dots,an improved algorithm for tracking infrared dim dots build upon meta-learning was adopted.Firstly,the pre-training tracking model was used to apply the meta-learning to the convolutional neural network.The general representation of the target were obtained through the offline training on the static infrared image data set,accordingly to obtain the specific representation of the infrared point-like target by using the initial frame target position.The target motion model was predicted by kalman filter algorithm and the optimal search area was obtained.In addition,in order to solve the problem of target loss caused by occlusion,the re-detection mechanism was studied.Theoretical analysis and experimental verification were carried out,with tracking accuracy up to 90%.Concluded that this approach is capable of tracking the infrared dim dots more accurately than other tracking algorithms in the same data set.This research provides a reference for the application of machine learning algorithms in the tracking of infrared dim and small targets.