Fast Video Object Segmentation Based on Siamese Networks

被引：0

作者：

Fu L.-H. ^{[1
]}

Zhao Y. ^{[1
]}

Sun X.-W. ^{[1
]}

Lu Z.-S. ^{[1
]}

Wang D. ^{[1
]}

Yang H.-X. ^{[1
]}

机构：

[1] Faculty of Information Technology, Beijing University of Technology, Beijing

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 04期

关键词：

Computer vision; Deep learning; Feature space; Siamese network; Video object segmentation;

D O I：

10.3969/j.issn.0372-2112.2020.04.001

中图分类号：

学科分类号：

摘要：

Video object segmentation (VOS) is a research hotspot in the field of computer vision.Traditional VOS based on deep learning fine-tunes the deep network online, which leads to long time-consuming segmentation and is difficult to meet real-time requirements.Therefore, we propose a fast VOS method.First, the weight-shared siamese encoder subnet maps the reference stream and the target stream to the same feature space; so that the same objects have similar features.Then, the global feature extraction subnet matches the features similar to the given object to locate the object.Finally, the decoder subnet restores the object features and gets edge information by connecting the low-level features of target stream to output the mask.Experiments on public benchmark datasets show that our method improves the speed significantly and achieves good performance. © 2020, Chinese Institute of Electronics. All right reserved.

引用

页码：625 / 630

页数：5

共 24 条

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