SA-Siam++:基于双分支孪生网络的目标跟踪算法

doi:10.13190/j.jbupt.2019-141

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (6): 105-110.doi: 10.13190/j.jbupt.2019-141

SA-Siam++:基于双分支孪生网络的目标跟踪算法

田朗, 黄平牧, 吕铁军

可信分布式计算与服务教育部重点实验室(北京邮电大学), 北京 100876

收稿日期:2019-07-09 出版日期:2019-12-28 发布日期:2019-11-15
通讯作者: 吕铁军(1956-),男,教授,博士生导师,E-mail:lvtiejun@tsinghua.org.cn. E-mail:lvtiejun@tsinghua.org.cn
作者简介:田朗(1995-),男,硕士生.
基金资助:
国家自然科学基金项目（61671072）

SA-Siam++: the Two-Branch Siamese Network-Based Object Tracking Algorithm

TIAN Lang, HUANG Ping-mu, Lü Tie-jun

Key Laboratory of Trustworthy Distributed Computing and Service(Beijing University of Posts and Telecommunications), Ministry of Education, Beijing 100876, China

Received:2019-07-09 Online:2019-12-28 Published:2019-11-15

摘要/Abstract

摘要： 为了解决SiamFC在目标快速移动、背景与前景相似、光照强烈等复杂场景下鲁棒性低的问题，提出了一种新的基于语义和外观双分支孪生网络的跟踪方法SA-Siam++，包括通过沙漏-通道注意力机制提取语义信息的语义分支和通过SiamFC提取外观信息的外观分支.此外，将AlexNet网络更换为经过改进的VGG-16网络能显著增加特征提取能力.在OTB-2013、OTB-2015、UAV123和VOT2018等目标跟踪标准数据集上进行了实验.实验结果表明，所提算法获得的测试结果相比现有主流算法有较大提高，平均帧率为49帧/s，满足实时性要求.

关键词: 孪生网络, 目标跟踪, 语义分支, 复杂场景, 通道注意力

Abstract: To deal with the problem of low robustness of SiamFC in complex scenarios in which the object is moving fast, the background is similar to the foreground, and the illumination is strong, a new tracking method called SA-Siam++ was proposed based on two-branch siamese network, including semantic branch which is used to extract semantic information through the hourglass-channel attention mechanism and the appearance branch which is used to extract appearance information through SiamFC. In addition, replacing the AlexNet network with an improved VGG-16 network can significantly increase the feature extraction capabilities. Finally, experiments were carried out on OTB-2013, OTB-2015, UAV123 and VOT2018 which are standard object tracking datasets. It is shown show that the obtained with the proposed algorithm are greatly improved compared with the existing mainstream algorithms, and the average frame rate reaches 49 FPS, that can meet the real-time requirements.

Key words: siamese network, object tracking, semantic branch, complex scenario, channel attention

中图分类号:

TN929.53

田朗, 黄平牧, 吕铁军. SA-Siam++:基于双分支孪生网络的目标跟踪算法[J]. 北京邮电大学学报, 2019, 42(6): 105-110.

TIAN Lang, HUANG Ping-mu, Lü Tie-jun. SA-Siam++: the Two-Branch Siamese Network-Based Object Tracking Algorithm[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(6): 105-110.

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SA-Siam++:基于双分支孪生网络的目标跟踪算法

SA-Siam++: the Two-Branch Siamese Network-Based Object Tracking Algorithm

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