基于核密度估计的前视红外小目标跟踪

Small target tracking in forward looking infrared imagery based on kernel density estimation

针对前视红外图像中小目标较难跟踪的问题,提出了一种基于核密度估计的跟踪方法.融合灰度与局部加权灰度信息熵特征,对目标模板与候选目标区域进行核密度估计,通过均值偏移算法最小化目标候选区域的核密度分布与模板的核密度分布之间的距离来实现跟踪.跟踪过程中,由于受光照、遮挡等因素影响,目标特征可能发生渐变或突变,以Bhattacharyya系数为准则,对目标模板进行自动更新,解决了不能及时更新或过更新引起跟踪失败的问题.实验验证了所提出方法能够对前视红外小目标进行鲁棒的跟踪.

To deal with difficulties inherit when tracking small targets in a forward looking infrared （FLIR） image, the authors proposed an approach based on kernel density estimation. The intensity and locally weighted intensity entropy were fused to model targets. Tracking was performed by computing the mean shift vector that minimizes the distance between the kernel distribution for the target cadidate area and the model. The target might change slowly or it can alter drastically if the illumination changes or the target is obscured by other objects during the course of tracking. A strategy was proposed to update the model based on the Bhattacharyya coefficient, thus overcoming the problem of tracking failures caused by the model being under-updated or over-updated. Experiments verified that the algorithm is robust in tracking small targets in FLIR image sequences.