基于特征融合与抗遮挡的卫星视频目标跟踪方法

Feature-fusion and anti-occlusion based target tracking method for satellite videos

卫星视频中的目标易受到遮挡和复杂环境干扰等影响,造成对目标的运动状态估计不够准确,导致目标跟踪失败。基于此,在核相关滤波(KCF)算法的基础上设计2种算法提高目标跟踪的成功率,实现鲁棒性的目标跟踪。通过提取目标的方向梯度直方图(HOG)特征、灰度特征和高斯曲率特征表述目标的外观模型;联合响应图的峰值和平均峰值相关能量(APCE)对目标的响应图进行自适应加权融合,并将融合后的响应图峰值作为置信度对目标的模型进行自适应更新;通过使用卡尔曼滤波的方法对遮挡的目标进行位置预测,当目标遮挡结束时,对目标进行重新跟踪,解决卫星视频中目标被遮挡的问题。大量实验结果表明:所改进的相关滤波算法对卫星视频中的目标跟踪,尤其是在复杂环境、目标被遮挡及场景光照发生变化的情况下,具有良好的效果,并且在目标跟踪的精度和成功率等方面都有很大的提高,为进一步对卫星视频中的目标跟踪奠定了基础。

Targets in satellite videos are susceptible to occlusion and interference from complex environments,resulting in inaccurate estimation of the target motion state,eventually leading to target tracking failure.Therefore,based on the kernelized correlation filter(KCF)algorithm,two algorithms are designed to improve the success rate of target tracking to achieve robust target tracking.Firstly,extracting the different features(HOG features,gray features and Gaussian curvature features)of the target,then adaptively weighted fusion is carried out on the correlation response of different features of the target,whose purpose is to improve the anti-interference ability of the target against complex environments;Secondly,calculating the weight according to the maximum and average peak correlation energy(APCE)of the target response patch and using it as the confidence level to adaptively update the target model;Finally,the issue of the target being occluded in satellite videos can be resolved by employing the Kalman filter method to anticipate the position of the occluded target after the occlusion of the target is over and reappears.Many experimental results show that the improved correlation filter algorithm has sound effects on target tracking,especially in complex environments,occluded targets,and illumination variation.The success rate and precision have dramatically improved,laying the foundation for further target tracking in satellite videos.