面向无人机的视觉目标跟踪算法:综述与展望  

UAV Visual Target Tracking Algorithms:Review and Future Prospect

作　　者：陈琳 刘允刚[1] CHEN Lin;LIU Yungang(School of Control Science and Engineering,Shandong University,Jinan 250061,China)

机构地区：[1]山东大学控制科学与工程学院,山东济南250061

出　　处：《信息与控制》2022年第1期23-40,共18页Information and Control

基　　金：国家自然科学基金(62033007,61873146);山东省重大科技创新工程项目(2019JZZY010433);山东省泰山学者攀登计划。

摘　　要：近年来,无人机因其小巧灵活、智能自主等特点被广泛应用于民用和军事等领域中,特别是搜索侦察过程中首要的目标跟踪任务。无人机视觉目标跟踪场景的复杂性和运动目标的多变性,使得目标特征提取及模型建立困难,对目标跟踪性能带来巨大的挑战。本文首先介绍了无人机视觉目标跟踪的研究现状,梳理了经典和最新的目标跟踪算法,特别是基于相关滤波的跟踪算法和基于深度学习的跟踪算法,并对比了不同算法的优缺点。其次,归纳了常用的目标跟踪数据集和性能评价指标。最后,展望了无人机视觉目标跟踪算法的未来发展趋势。In recent years,due to unmanned aerial vehicles(UAV)characteristics such as flexibility,intelligence,and autonomy,UAV has been widely used in military and civilian applications,especially the primary target tracking task in search and surveillance.However,due to the complexity of the scene environment and the variability of the moving targets in UAV visual target tracking,it is difficult to extract features and formulate a model for the target,bringing a significant challenge to the tracking performance.Therefore,we provide a review of UAV visual target tracking.First,we present the current state of researching visual target tracking,review the classical and latest tracking algorithms,especially those based on correlation filtering and deep learning,and compare the advantages and disadvantages of different algorithms.Then,we summarize the commonly-used UAV target tracking datasets and evaluation metrics.Finally,we present a prospect of the future directions for developing UAV visual target tracking algorithms.

关 键 词：无人机 目标跟踪 相关滤波 深度学习 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]