摘要
针对现有高光谱视频目标跟踪算法在目标尺度发生变化时容易出现跟踪精度下降的问题,提出一种基于光谱匹配降维和特征融合的高光谱目标跟踪算法。首先,利用目标局部光谱和阈值来估计目标光谱,并利用目标光谱与高光谱图像进行朴素相关,实现高光谱图像降维,从而提取目标的深度特征。然后,利用局部方差判断目标区域,提取目标的3D方向梯度直方图(HOG)特征。为保留高光谱图像的光谱信息以及深度特征的语义信息,利用通道卷积融合的方法,得到更具辨别力的融合特征。最后,将融合特征送入相关滤波器,通过尺度池思想提高算法在目标尺度变化挑战下的跟踪鲁棒性。实验结果表明,所提跟踪算法在目标尺度变化挑战下具有更好的性能。
Objective Spectral features in hyperspectral video(HSV)enhance the ability to identify similar targets.However,HSV has high dimensions and a large amount of data,which causes great difficulties and high computing costs for feature extraction,and thus it is difficult to apply target tracking technology to HSV.In recent years,the development of snapshot hyperspectral technology has made it possible to acquire HSV.Many researchers have also turned their focus to HSV target tracking technology.In many target tracking processes,the target scale often changes to result in failed algorithm tracking.How to track targets robustly under the scale variations is an urgent problem to be solved.Methods The algorithm is based on the correlation filtering framework and the scale-adaptive kernel correlation filter tracker.We employ the difference between the spectral curve of each pixel and the local spectral curve of the target and count the error value to segment the target pixel and the background pixel.The target spectral curve is obtained by averaging the target pixels,and the dimensionality reduction is realized by adopting the simple correlation between the target spectral curve and the image.Meanwhile,the dimensionally reduced image is input into the MobileNet V2 to extract deep features.The target area is judged by the local variance,and the 3D histogram of oriented gradient(HOG)features of the target are enhanced.To preserve the unique spectral information of hyperspectral images and the semantic information of deep features,we utilize the method of channel convolution fusion to obtain more discriminative deep convolution HOG features which are fed into the filter to adapt to scale variations through the scale pooling idea.Results and Discussions Three hyperspectral target tracking algorithms are selected for comparison in the experiment to verify the effectiveness of the proposed algorithm.Additionally,the results are presented in the experimental sequence for visualizing the performance of the algorithm.Fig.7 presents
作者
郭业才
曹佳露
韩莹莹
张恬梦
赵东
陶旭
Guo Yecai;Cao Jialu;Han Yingying;Zhang Tianmeng;Zhao Dong;Tao Xu(School of Electronics&Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;School of Electronics and Information Engineering,Wuxi University,Wuxi 214105,Jiangsu,China;No.703 Research Institute of China State Shipbuilding Corporation Limited,Harbin 150000,Heilongjiang,China;College of Aerospace and Civil Engineering,Harbin Engineering University,Harbin 150000,Heilongjiang,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2023年第20期144-153,共10页
Acta Optica Sinica
基金
国家自然科学基金(62001443,62105258)
江苏省自然科学基金(BK20210064)
山东省自然科学基金(ZR2020QE294)
江苏省高等学校基础科学(自然科学)研究面上项目(22KJB140015)
无锡市创新创业资金“太湖之光”科技攻关计划(基础研究)项目(K20221046,K20221043)
无锡学院人才启动基金(2021r007,2021r008)。
关键词
测量
目标跟踪
高光谱视频
光谱匹配降维
通道卷积融合
measurements
target tracking
hyperspectral video
spectral matching dimensionality reduction
channel convolution fusion
