摘要
在视觉目标跟踪领域,长时跟踪因存在更为复杂的遮挡、相似物干扰和目标消失等具有现实意义的挑战场景,而越来越被研究者所重视。传统长时跟踪算法存在精度低和效率低等问题,已经无法满足如视频监控和自动驾驶等领域对跟踪器性能的应用需求。目前,大量的研究工作通过引入深度神经网络快速推动了长时跟踪技术的发展。为了深入分析深度长时跟踪算法的现状与未来发展,通过对比长短时跟踪数据集及评价指标,初步界定了长时跟踪任务范畴,归纳了长时跟踪任务的需求和难点,并介绍了长时跟踪数据集及评价体系的发展。基于深度长时目标跟踪算法的设计框架,详细描述了框架各组成部分的设计思路。以长时跟踪策略为切入点深入分析了现有研究工作,归纳了不同模型的优缺点及特性。依据对现有研究工作的整理和总结,讨论了该领域面临的挑战,并对未来的发展方向进行了展望。
In the field of visual target tracking,long-term tracking has been paid more and more attention by researchers,because it contains more realistic challenging scenarios,such as occlusion,similar object interference and target disappearance.However,traditional long-term tracking algorithms are inefficient and have been unable to meet the application requirements of tracker performance in fields,such as video surveillance and autonomous driving.Recently,a lot of work has rapidly advanced the development of long-term tracking techniques by introducing deep neural networks.In order to analyze the current situation and future development of deep-learning-based long-term tracking algorithms,firstly,by comparing the long-term and short-term tracking datasets and their evaluation indicators,the requirements and difficulties of longterm tracking tasks are summarized,and the development of long-term tracking datasets and evaluation systems is introduced.Subsequently,based on the design framework of deep-learning-based long-term tracking algorithm,the design ideas of each component of the framework are described in detail.Then,taking the long-term tracking strategy as the starting point,the existing research work is analyzed,and the advantages and disadvantages of different models and their characteristics are summarized.Finally,based on the summary of existing research work,the challenges faced in this field are discussed,and the future research trends are presented.
作者
梁义涛
韩永波
李磊
LIANG Yitao;HAN Yongbo;LI Lei(College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China;Henan Key Laboratory of Grain Photoelectric Detection and Control,Zhengzhou 450001,China)
出处
《计算机工程与应用》
CSCD
北大核心
2023年第4期1-17,共17页
Computer Engineering and Applications
基金
国家自然科学基金(31171775)
河南省教育厅重点科研项目(17A510008)
河南工业大学创新基金重点项目(2020ZKCJ02)。
关键词
视觉目标跟踪
跟踪数据集
长时跟踪
实时跟踪
深度神经网络
visual object tracking
tracking datasets
long-term tracking
real-time tracking
deep neural network
