Multi-layer features template update object tracking algorithm based on SiamFC++

IF 2.4 4区 计算机科学
Xiaofeng Lu, Xuan Wang, Zhengyang Wang, Xinhong Hei
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引用次数: 0

Abstract

SiamFC++ only extracts the object feature of the first frame as a tracking template, and only uses the highest level feature maps in both the classification branch and the regression branch, so that the respective characteristics of the two branches are not fully utilized. In view of this, the present paper proposes an object tracking algorithm based on SiamFC++. The algorithm uses the multi-layer features of the Siamese network to update template. First, FPN is used to extract feature maps from different layers of Backbone for classification branch and regression branch. Second, 3D convolution is used to update the tracking template of the object tracking algorithm. Next, a template update judgment condition is proposed based on mutual information. Finally, AlexNet is used as the backbone and GOT-10K as training set. Compared with SiamFC++, our algorithm obtains improved results on OTB100, VOT2016, VOT2018 and GOT-10k data sets, and the tracking process is real time.

Abstract Image

基于 SiamFC++ 的多层特征模板更新物体跟踪算法
SiamFC++ 只提取第一帧的物体特征作为跟踪模板,在分类分支和回归分支中都只使用最高级别的特征图,这样就不能充分利用两个分支各自的特点。有鉴于此,本文提出了一种基于 SiamFC++ 的物体跟踪算法。该算法利用连体网络的多层特征来更新模板。首先,利用 FPN 从 Backbone 的不同层提取特征图,用于分类分支和回归分支。其次,利用三维卷积更新物体跟踪算法的跟踪模板。接着,提出了基于互信息的模板更新判断条件。最后,使用 AlexNet 作为骨干网,GOT-10K 作为训练集。与 SiamFC++ 相比,我们的算法在 OTB100、VOT2016、VOT2018 和 GOT-10k 数据集上获得了更好的结果,并且跟踪过程是实时的。
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来源期刊
Eurasip Journal on Image and Video Processing
Eurasip Journal on Image and Video Processing Engineering-Electrical and Electronic Engineering
CiteScore
7.10
自引率
0.00%
发文量
23
审稿时长
6.8 months
期刊介绍: EURASIP Journal on Image and Video Processing is intended for researchers from both academia and industry, who are active in the multidisciplinary field of image and video processing. The scope of the journal covers all theoretical and practical aspects of the domain, from basic research to development of application.
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