摘要
为解决现有高光谱视频跟踪算法在光照变化情况下跟踪效果不佳的问题,提出一种基于深度谱三元级联特征的高光谱目标跟踪算法.首先,设置阈值,利用目标局部光谱曲线将目标与背景分割,利用波段匹配获得目标光谱曲线,计算结构张量获得目标光谱权重曲线.然后,利用目标光谱曲线与高光谱图像进行光谱角距离操作完成高光谱图像降维,并提取目标深度特征.提取目标图像的尺度不变局部三值模式(SILTP)特征,并分配相应的光谱权重,融入光谱信息,得到谱三元级联(STC)特征.将降维后的目标深度特征与STC特征按通道卷积,得到更具判别性和鲁棒性的深度谱三元级联(DSTC)融合特征.最后,将DSTC融合特征送入双相关滤波器(DCF).实验结果表明,与已有的先进算法相比,所提出的跟踪算法在光照变化挑战下具有更好的跟踪性能.
Abstract
To address the issue of decreased tracking accuracy due to variation in illumination in hyperspectral target tracking tasks,a hyperspectral target tracking algorithm based on deep spectral-ternary concatenated(DSTC)features is proposed.A threshold can be initially set to segment the target from the background by utilizing the local spectral curve of the target.The spectral curve of the target can be captured by utilizing band matching,and the spectral weight curve of the target can be derived by computing the structural tensor.Subsequently,dimensionality reduction of the hyperspectral image can be accomplished by performing spectral angle distance operation between the spectral curve of the target and hyperspectral image.Hence,the deep features of the target can be extracted.The scale invariant local ternary pattern(SILTP)features are extracted from the target image.Then,the spectral weights are allocated to SILTP features,and SILTP features are integrated with spectral information to derive the spectral-ternary concatenated(STC)features.The dimension-reduced target deep features and STC features are convolved by channels to obtain more discriminative and robust DSTC fusion features.Finally,the fused DSTC features are fed into the dual correlation filter.The experimental results demonstrate that the tracking algorithm proposed in this study exhibits superior tracking performance under the challenge of illumination variations when compared to the current state-of-the-art algorithms.
NETL
NSTL
万方数据