针对复杂天空背景条件下低信噪比的红外弱小目标跟踪问题, 设计了一种多目标跟踪系统。首先计算红外图像的光流场, 结合阈值分割和形态学滤波等数学方法检测出目标; 在该结果的基础上, 结合目标运动的连续性, 运用邻域轨迹预测的方法滤除检测过程中产生的噪声; 随后运用卡尔曼滤波轨迹预测的方法解决在跟踪过程中目标丢失的问题, 并解决当多目标轨迹出现交联时如何辨识出各个目标轨迹的问题。该系统充分运用了目标的运动特性避免了噪声的干扰和目标轨迹混淆。使用长波红外热像仪采集的红外序列图像对系统进行了验证, 实验结果及相应理论分析表明该系统可有效实现复杂背景下的红外弱小目标跟踪。

A multi-target tracking framework is designed to detect small infrared targets under complex backgrounds when the signal-to-noise-ratio (SNR) is low. The infrared image’s optical flow field is calculated, based on which the mathematic methods inclnding segmentation of threshold and mathematical morphological filter are employed to detect the infrared target from the backgrounds. Based on the results, with the target moving continuously, the neighborhood predict arithmetic is used to we eliminate the noise generated in the process of detection. The track prediction method based on Kalman filter is given to solve the problems of target missing and track crossover. This framework avoids the interference of the noise and the confusion of multi-track through the moving characteristic of targets. The infrared sequence images got from long-wavelength infrared camera verify that this framework is effective to track the small infrared targets under complex backgrounds.