@inproceedings{2a584d121a0c489c9c61e61e2ba8a5f4,
title = "Robust Real-Time Visual Object Tracking via Multi-Scale Fully Convolutional Siamese Networks",
abstract = "Robust visual object tracking against occlusions and deformations is still very challenging task. To tackle these issues, existing Convolutional Neural Networks (CNNs) based trackers either fail to handle them or can just run in low speed. In this paper, we present a realtime tracker which is robust to occlusions and deformations based on a Region-based, Multi-Scale Fully Convolutional Siamese Network (R- MSFCN). In the proposed R-MSFCN, the information of regions is extracted separately by the proposition of position-sensitive score maps on multiple convolutional layers. Combining these score maps via adaptive weights leads to accurate location of the target on a new frame. The experiments illustrate that our method outperforms state-of-the-art approaches, and can handle the cases of object deformation and occlusion at about 31 FPS.",
author = "Longchao Yang and Peilin Jiang and Fei Wang and Xuan Wang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017 ; Conference date: 29-11-2017 Through 01-12-2017",
year = "2017",
month = dec,
day = "19",
doi = "10.1109/DICTA.2017.8227487",
language = "英语",
series = "DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--7",
editor = "Yi Guo and Manzur Murshed and Zhiyong Wang and Feng, \{David Dagan\} and Hongdong Li and Cai, \{Weidong Tom\} and Junbin Gao",
booktitle = "DICTA 2017 - 2017 International Conference on Digital Image Computing",
}