复杂天空背景下的红外弱小目标跟踪

为解决传统跟踪算法不能有效区分复杂天空云层背景边缘和红外弱小目标，从而在跟踪过程中产生“偏移”的问题。在时空上下文原理基础上分析跟踪“偏移”的原因，引入高斯曲率滤波，提出一种改进的时空上下文红外弱小目标跟踪算法。该算法首先采用高斯曲率滤波对上下文区域进行预处理，在保留上下文区域背景边缘的同时剔除高频的红外弱小目标和噪声，从而获得准确的红外弱小目标置信图，利用红外弱小目标置信图估计出红外弱小目标位置。采用四组复杂天空背景下的红外弱小目标图像序列进行实验，并与经典的模板匹配算法、基于粒子滤波的均值漂移算法和快速压缩跟踪算法三种跟踪算法作比较。实验结果表明，算法在主观视觉和客观评价指标方面均优于其他三种算法，具有更高的目标跟踪精度与较好的实时性，可以实现对复杂天空背景下红外弱小目标的有效跟踪。

Tracking of infrared dim small target in complex sky background

The complex sky cloud background edge and infrared dim small target cannot be distinguished using traditional tracking algorithms, and the "deviation" problem is produced when tracking the target. To solve this problem, this paper proposes a target tracking algorithm based on Gaussian curvature filter (GCF) and spatio-temporal context (STC) after introducing the STC theory and analyzing the reason of "deviation". First of all, the context areas are dealt with GCF which can reserve the image edge and eliminate the noise and the infrared dim small target, so as to obtain the infrared dim small target's accurate confidence map. Then the infrared dim small target's coordinates are confirmed by the confidence map. Four groups of the infrared dim small target sequences in the complex sky background experimental results prove that the proposed algorithm is effective, compared with several classical algorithms, such as TMT, MS-PF and FCT. The proposed algorithm has better performance than the three traditional algorithms on subjective vision and objective evaluating indicator and shows higher tracking accuracy and better real time performance. The infrared dim small target in complex sky cloud background can be effectively tracked with this algorithm.