摘要
在计算机视觉领域中,基于孪生网络的跟踪算法相比于传统算法提高了精度和速度,但是仍会受到目标遮挡、变形、环境变化等影响,导致孪生网络的跟踪算法的性能降低。为了深入了解基于孪生网络的单目标跟踪算法,本文对现有基于孪生网络目标跟踪算法进行了总结和分析,主要包括在孪生网络中引入注意力机制方法、超参数推理方法和模板更新方法,对这3种方法的目标跟踪算法进行了综述,详细介绍了国内外近几年基于孪生网络的算法研究和发展现状。对3个方面的代表算法采用VOT2016、VOT2017、VOT2018和OTB-2015数据集进行实验对比,获得了多种基于孪生网络的目标跟踪算法的性能。最后对基于孪生网络的目标跟踪算法进行了总结,并对未来的发展方向进行了展望。
In the field of computer vision,twin network-based tracking algorithms improve accuracy and speed in comparison with traditional algorithms,but they are still affected by target occlusion,deformation,and environmental changes,which leads to the performance degradation of twin network-based tracking algorithms.In order to gain an in-depth understanding of the single target tracking algorithm based on twin networks,the existing target tracking algorithms based on twin networks are summarized and analyzed,mainly including the introduction of attention mechanism method,hyper-parameter inference method and template update method in twin networks,which reviews target tracking algorithms of these three methods and introduces in detail the research and development status of algorithms based on twin networks at home and abroad in recent years.The representative algorithms of the three aspects are experimentally compared using VOT2016,VOT2017,VOT2018 and OTB-2015 datasets to obtain the performance of multiple twin network-based target tracking algorithms.Finally,the twin network-based target tracking algorithms are summarized and the future development direction is prospected.
作者
贺泽民
曾俊涛
袁宝玺
梁德建
苗宗成
HE Zemin;ZENG Juntao;YUAN Baoxi;LIANG Dejian;MIAO Zongcheng(Technological Institute of Materials&Energy Science,Xijing University,Xi'an 710123,China;Beijing Xinghang Electromechanical Equipment Co.Ltd.,Beijing 100074,China;School of Artificial Intelligence,Optics and Electronics,Northwestern Polytechnical University,Xi'an 710072,China)
出处
《液晶与显示》
CAS
CSCD
北大核心
2024年第2期192-204,共13页
Chinese Journal of Liquid Crystals and Displays
基金
国家重点研发计划(No.2022YFB3603703)
国家自然科学基金(No.52173263)
陕西省秦创原引用高层次创新创业人才项目(No.QCYRCXM-2022-219)。
关键词
计算机视觉
目标跟踪
孪生网络
深度学习
computer vision
target tracking
Siamese networks
deep learning
