[1]姜文涛,孟庆姣.自适应时空正则化的相关滤波目标跟踪[J].智能系统学报,2023,18(4):754-763.[doi:10.11992/tis.202202030]
JIANG Wentao,MENG Qingjiao.Correlation filter tracking for adaptive spatiotemporal regularization[J].CAAI Transactions on Intelligent Systems,2023,18(4):754-763.[doi:10.11992/tis.202202030]
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JIANG Wentao,MENG Qingjiao.Correlation filter tracking for adaptive spatiotemporal regularization[J].CAAI Transactions on Intelligent Systems,2023,18(4):754-763.[doi:10.11992/tis.202202030]
自适应时空正则化的相关滤波目标跟踪
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第4期
页码:
754-763
栏目:
学术论文—机器感知与模式识别
出版日期:
2023-07-15
- Title:
- Correlation filter tracking for adaptive spatiotemporal regularization
- Keywords:
- spatiotemporal adaptation; local response; global response; neural network; convolutional neural network; feature extraction; dimension reduction; principal component analysis algorithm
- 分类号:
- TP391.4
- 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，提出了一种自适应时空正则化的新方法，从而提高算法在目标跟踪过程中适应外观变化的鲁棒性。首先在目标函数中引入空间局部响应变化量，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；最后引入卷积神经网络进行深度特征提取，为减少高维数据存储过大，采用主成分分析算法进行降维处理，既保留主要特征又加快计算速度。在数据集OTB2013和OTB2015上的平均精确率和平均成功率相较于时空正则化相关滤波器算法分别提高了4.7%和12.7%。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满足实时性需求。
- Abstract:
- The regularization filter defines the regularization term in advance, but it can not suppress the learning of non-target region in real time. Aiming at this shortcoming, a new adaptive spatiotemporal regularization method is proposed to improve robustness of the algorithm to adapt to appearance change in the process of target tracking. The method firstly introduces the spatial local response variation into the objective function, so that the filter can focus on the trusted part of the learning object, obtaining the response model. Then, the update rate of the filter is determined based on the change of global response. Finally, the convolutional neural network is introduced for deep feature extraction. In order to reduce the storage capacity of high-dimensional data, the principal component analysis algorithm is employed to reduce dimension, which not only retains the main features, but also speeds up the calculation. Comparing with the spatial-temporal regularized correlation filtering algorithms, the average accuracy and success rate of data sets OTB2013 and OTB2015 are respectively increased by 4.7% and 12.7%. A large number of experiments show that the algorithm basically meets the real-time requirements in complex background, object occlusion, fast motion and other scenes.
- 参考文献/References:
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备注/Memo
收稿日期:2022-02-28。
基金项目:国家自然科学基金项目(61172144)；辽宁省自然科学基金项目(20170540426)；辽宁省教育厅基金项目(LJYL049).
作者简介:姜文涛，副教授，博士，主要研究方向为图像与视觉计算、模式识别与人工智能。主持国家自然科学基金项目2项，发表学术论文20余篇;孟庆姣，硕士硕士生，主要研究方向为图像与视觉计算、模式识别与人工智能
通讯作者:姜文涛.E-mail:704123343@qq.com
更新日期/Last Update:
1900-01-01