文献类型：期刊文献

中文题名：学习时空一致性相关滤波的视觉跟踪

英文题名：Learning temporal-spatial consistency correlation filter for visual tracking

作者：朱建章[1];王栋[2];卢湖川[2]

第一作者：朱建章

机构：[1]河南财经政法大学数学与信息科学学院,郑州450046;[2]大连理工大学信息与通信工程学院,大连116024

第一机构：河南财经政法大学数学与信息科学学院

年份：2020

卷号：50

期号：1

起止页码：128-150

中文期刊名：中国科学：信息科学

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：国家自然科学基金(批准号:61502070,61725202);河南省自-然科学基金(批准号:18A110013)资助项目。

语种：中文

中文关键词：视觉跟踪;相关滤波;时空一致性;正则化;共轭梯度下降

外文关键词：visual tracking;correlation filter;temporal-spatial consistency;regularization;conjugate gradient descent

摘要：判别相关滤波跟踪算法通过对中心目标块(唯一准确正样本)循环移位获取训练集,依赖潜在样本周期延拓假设,使得模型训练和检测可以通过快速傅里叶变换高效完成,然而整个学习过程没有对真正的背景信息进行建模.背景感知相关滤波(BACF)跟踪算法利用一个二进制掩码矩阵通过密集采样的方法获取真正的正、负样本对目标外观进行建模,然而BACF算法在学习相关滤波器时并没有考虑滤波器的时间一致性和空间一致性信息,当目标出现外观突变时,学习到的相关滤波器将会偏向背景而发生漂移.为了解决学习到的相关滤波器适应连续帧之间的外观突变问题,本文在基准BACF算法框架下引入时间一致性约束项和空间一致性约束项,提出了学习时空一致性相关滤波(TSCF)跟踪算法.时间一致性约束项在时间序列意义上起到平滑多通道相关滤波的作用;空间一致性约束项在空间分布意义上平滑多通道相关滤波,使得学习到的相关滤波能量分布更加均匀.本文的TSCF模型有闭式解,采用共轭梯度下降法迭代逼近模型的最优解,且优化过程利用循环矩阵性质转化到傅里叶域快速求解,有效降低计算大型矩阵的代价.本文的TSCF算法跟踪结果在TB100公开数据库上显示,距离精度较基准BACF算法提升了5.5%,成功率曲线图线下面积(AUC)提升了4.3%,纯手工特征跟踪性能在TB100数据库上100个视频的跟踪距离精度达到0.879,AUC为0.664,结果展示本文的TSCF算法在遇到诸如短时间遮挡和面内旋转或面外旋转等挑战性问题时具有一定的鲁棒性和有效性.
Discriminant correlation filter-based tracking approaches,which adopt a circular shift operator on the tracking target object(the only accurate positive sample)to obtain training data and rely on the potential sample periodic extension hypothesis that enables model training and detection,can be efficiently accomplished through FFT.However,real background information is not modeled during the total learning process.The backgroundaware correlation filter(BACF)tracking algorithm uses a binary matrix to acquire real positive and negative samples using a dense sampling method to model the target’s appearance.However,the BACF algorithm does not consider temporal and spatial consistency information,and when a target undergoes an abrupt change,the learned correlation filter will drift to the background.To solve this problem,in this paper,we introduce temporal and spatial consistency constraints into the baseline BACF framework and propose a learning temporal-spatial consistency correlation filter(TSCF)tracking algorithm.This enables the correlation filter to learn to adapt to the appearance of mutation between successive frames.The temporal consistency constraint smooths the multichannel correlation filter in the time series,and the spatial consistency constraint smooths the multi-channel correlation filter in spatial distribution,thus making the energy distribution more uniform of the correlation filter learned.In this paper,the TSCF model has closed solutions and the conjugate gradient descent method is used to approximate the optimal solution of a system of closed solutions.The optimization process can then be transformed into the Fourier domain using cyclic matrix properties to quickly obtain a solution,which effectively reduces the cost of calculating large matrices.In this paper,our TSCF algorithm increases distance precision by 5.5%and raises the AUC by 4.3%compared to the baseline BACF algorithm on the TB100 public database.The distance precision achieves 0.879 and the AUC reaches 0.663 on the TB100 database making use of only hand-crafted features.The TSCF algorithm proposed in this paper can be applied to challenging conditions such as short time occlusion,out-of-plane rotation,in-plane rotation,and so on,thus demonstrating its robustness and effectiveness.