摘要
针对复杂场景中传统单一手工特征表达能力不足,以及模型更新过程中由于误差累积导致模型退化问题,提出了基于相关滤波融合卷积残差学习的目标跟踪算法.将融合了多特征的相关滤波算法定义为神经网络中的一层,将特征提取、响应图生成、模型更新整合到端到端的神经网络中进行模型训练;为解决在线更新过程中模型退化问题,引入残差学习方式引导模型更新.在基准数据集OTB-2013和OTB-2015上的实验结果表明,本文算法能够有效应对复杂场景中运动模糊、形变和光照等变化,具备较高跟踪精度与鲁棒性.
Aiming at the problem of insufficient expression ability of traditional single manual feature and model degradation caused by error accumulation in the process of model updating in complex scenes,Based on this,the object tracking algorithm based on correlation filtering and convolution residual learning is proposed.The multifeature correlation filtering algorithm is defined as a layer in the neural network,and the feature extraction,response graph generation,and model update are integrated into the end-to-end neural network for model training.In order to reduce the degradation of model during online updating,the residual learning mode is introduced to guide model updating.The proposed method is validated on the benchmark datasets OTB-2013 and OTB-2015.The experimental results show that the proposed algorithm can effectively deal with motion blur,deformation,and illumination in the complex scene,and has high tracking accuracy and robustness.
作者
杨亚光
尚振宏
Yang Yaguang;Shang Zhenhong(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming,Yunnan 650500,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第12期173-180,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61462052)。
关键词
图像处理
目标跟踪
相关滤波
端到端学习
残差学习
image processing
object tracking
correlation filter
end-to-end learning
residuals learning
作者简介
尚振宏,E-mail:shangzhenhong@126.com;杨亚光,E-mail:1107298031@qq.com。
