Combined kalman filter and multifeature fusion siamese network for real-time visual tracking

doi:10.3390/s19092201

CORC > 光电技术研究所 > 中国科学院光电技术研究所 > 光电工程总体研究室（一室）

	Combined kalman filter and multifeature fusion siamese network for real-time visual tracking
	Zhou, Lijun 1,2; Zhang, Jianlin 2
刊名	Sensors (Switzerland)
	2019-05-01
卷号	19 期号:9
ISSN号	1424-8220
DOI	10.3390/s19092201
文献子类	期刊论文
英文摘要	SiamFC has a simple network structure and can be pretrained offline on a large data set, so it has attracted the attention of many researchers. It has no online learning process at all. Hence, there are no good solutions for some complex tracking scenarios such as occlusion and large target deformation. For this problem, we propose a method using the Kalman filter method and fusion multiresolution features and get multiple response scores. The Kalman filter acquires the target’s trajectory information, which is used to process complex tracking scenes and to change the selection method of the search area. This also enables our tracker to stably track fast moving targets. The introduction of the Kalman filter compensates for the shortcomings that SiamFC can only track offline, and the tracking network has an online learning process. The fusion of multiresolution features to obtain multiple response scores map helps the tracker to obtain robust features that can be adapted to a variety of tracking targets. Our proposed method has reached the state-of-the-art in testing on five data sets and can be run in real time (40 fps), including OTB2013, OTB2015, OTB50, VOT2015 and VOT 2016. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
语种	英语
出版者	MDPI AG, Postfach, Basel, CH-4005, Switzerland
内容类型	期刊论文
源URL	[http://ir.ioe.ac.cn/handle/181551/9810]
专题	光电技术研究所_光电工程总体研究室（一室）
作者单位	1.University of Chinese Academy of Sciences, Beijing; 100000, China 2.Key Laboratory of Optical Engineering, Institute of Optics and Electronics, Chinese Academy of Sciences, No.1, Optoelectronic Avenue, Wenxing Town, Shuangliu District, Chengdu; 610200, China;
推荐引用方式 GB/T 7714	Zhou, Lijun,Zhang, Jianlin. Combined kalman filter and multifeature fusion siamese network for real-time visual tracking[J]. Sensors (Switzerland),2019,19(9).
APA	Zhou, Lijun,&Zhang, Jianlin.(2019).Combined kalman filter and multifeature fusion siamese network for real-time visual tracking.Sensors (Switzerland),19(9).
MLA	Zhou, Lijun,et al."Combined kalman filter and multifeature fusion siamese network for real-time visual tracking".Sensors (Switzerland) 19.9(2019).