融合检测技术的孪生网络跟踪算法综述  

A survey of siamese networks tracking algorithm integrating detection technology

作　　者：张津浦 王岳环[1] Zhang Jinpu;Wang Yuehuan(School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学人工智能与自动化学院,湖北武汉430074

出　　处：《红外与激光工程》2022年第10期308-321,共14页Infrared and Laser Engineering

摘　　要：近年来,基于孪生网络的方法在视觉目标跟踪中取得了巨大的进步,但是这类方法在处理跟踪中的目标状态估计以及复杂场景干扰中仍存在较大的提升空间。随着深度学习在目标检测领域取得的成功,越来越多的研究将其成果用于指导目标跟踪技术的发展。对融合检测技术的孪生目标跟踪算法进行了综述。首先介绍检测和跟踪的联系与区别,同时分析检测技术对改进基于孪生网络的跟踪算法的可行性;然后阐述在不同检测框架指导下的孪生目标跟踪算法,以及使用OTB100、VOT2018、GOT-10k和LaSOT公开数据集对各类算法进行对比和分析;最后对全文进行总结,并对目标跟踪的未来发展方向进行展望。In recent years,siamese tracking networks have achieved promising performance in visual tracking.However,there is still large room for improvement in the challenge of target state estimation and complex aberrances for siamese trackers.With the success of deep learning in object detection,more and more object detection technologies are used to guide object tracking.This survey reviews the siamese tracking algorithms integrating detection technologies.Firstly,we introduce the relation and difference between detection and tracking,and analyze the feasibility of improving siamese tracking algorithms by detection technologies.Then,we elaborate the existing siamese trackers based on different detection frameworks.Furthermore,we conduct extensive experiments to compare and analyze the representative methods on the popular OTB100,VOT2018,GOT-10k,and LaSOT benchmarks.Finally,we summarize our manuscript and prospect the further trends of visual tracking.

关 键 词：目标跟踪 深度学习 孪生网络 目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]