多特征感知的时空自适应相关滤波目标跟踪  

Multi-feature-aware Spatiotemporal Adaptive Correlation Filtering Target Tracking

作　　者：孟庆姣 姜文涛[1] MENG Qingjiao;JIANG Wentao(School of Software,Liaoning Technical University,Huludao,Liaoning 125105,China)

机构地区：[1]辽宁工程技术大学软件学院,辽宁葫芦岛125105

出　　处：《计算机科学》2023年第S02期191-199,共9页Computer Science

基　　金：国家自然科学基金(61172144);辽宁省自然科学基金(20170540426);辽宁省教育厅基金(LJYL049)。

摘　　要：针对正则化滤波器预先定义正则化项,但无法实时抑制非目标区域学习的缺点,提出了一种多特征感知的时空自适应相关滤波目标跟踪的新方法。首先在目标函数中引入空间局部响应变化量实现空间正则化,使滤波器专注于学习对象中值得信任的部分,从而得到响应模型;其次根据全局响应变化决定滤波器的更新率;最后通过级联颜色直方图(Colour Name,CN)与降维后的梯度直方图(Fast Histogram of Oriented Gradient,fHOG)特征实现非卷积特征层面的融合,采用ImageNet-VGG-2048的Conv1,Conv5层提取目标的空间轮廓以及语义信息,并使用ReLU函数拟合训练数据,在保留主要信息的同时提高速率。在数据集DTB70上的精确率(0.747)和成功率(0.789)相较于STRCF算法的精确率(0.737)和成功率(0.760)分别提高了1%和2.9%。大量实验证明该算法在复杂背景、物体遮挡、快速运动等多种场景下基本能满足实时性需求。Aiming at the disadvantage that the regularization filter defines the regularization term in advance but cannot suppress the learning of non-target region in real time,a new method of multi-feature-aware spatiotemporal adaptive correlation filtering target tracking is proposed.Firstly,the spatial local response variation is introduced into the objective function to realize the spatial regularization,so that the filter can focus on the trustworthy part of the learning target,and then the response model is obtained.Secondly,the update rate of the filter is determined according to the change of the global response.Finally,the non-convolution feature level fusion is realized by cascading color histograms(CN)and dimensionally reduced gradient histograms(fHOG).The conv1 and conv5 layers of imagenet-vgg-2048 are used to extract the spatial contour and semantic information of the target.The ReLU function is used to fit and train the data to improve the speed while retaining the main information.Results:In this paper,we compared 8 algorithms of the same type,and used the defined baseline algorithm STRCF(2018)in the objective function,and KCF(2014),which introduces gauss kernels to increase computational speed and sample circularly using a cyclic matrix,and MOSSE_CA(2021),which links context and scale filters,and DCF_CA(2017),which increases the number of samples but reduces the search area Staple(2016)with temporal regularization;region constraint to reduce anomalous ARCF(2019);correlation filter HSTDCF_CA(2021)with hierarchical spatiotemporal map regularization;and target segmentation into four blocks,the SAME_CA(2020)of the scale factor is calculated by using the kernel correlation filter to find the maximum response position of each block.Compared with the accuracy(0.737)and success rate(0.760)of STRCF algorithm,the accuracy rate(0.747)and success rate(0.789)of DTB70 were increased by 1%and 2.9%respectively.Conclusion:The image information learned after multi-layer feature fusion is updated to obtain the overall contou

关 键 词：目标跟踪 相关滤波 时空自适应 局部响应与全局响应 卷积神经网络 特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]