中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Robust visual tracking via scale-and-state-awareness

文献类型：期刊论文

作者Qin, Lei3; Zhang, Shengping2; Qi, Yuankai4; Yao, Hongxun4; Huang, Qingming1,4
刊名NEUROCOMPUTING
出版日期2019-02-15
卷号329页码:75-85
关键词Visual tracking Convolutional neural network Bounding box refinement Occlusion awareness
ISSN号0925-2312
DOI10.1016/j.neucom.2018.10.035
英文摘要Convolutional neural networks (CNNs) have been applied to visual tracking with demonstrated success in recent years. However, the performance of CNN-based trackers can be further improved, because the predicted upright bounding box cannot tightly enclose the target due to factors such as deformations and rotations. Besides, many existing CNN-based trackers neglect to distinguish the occluded state of the target from non-occluded states, which causes the samples collected during occlusions wrongly update the tracker to focus on other objects. To address these problems, we propose to adaptively utilize the level set segmentation and bounding box regression techniques to obtain a tight enclosing box, and design a CNN to recognize whether the target is occluded. Extensive experimental results on a large benchmark dataset demonstrate the effectiveness of the proposed method compared to several state-of-the-art tracking algorithms. (C) 2018 Elsevier B.V. All rights reserved.
资助项目National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61572465] ; National Natural Science Foundation of China[61390510] ; National Natural Science Foundation of China[61732007] ; National Natural Science Foundation of China[61872112] ; National Natural Science Foundation of China[61772158] ; National Natural Science Foundation of China[61472103] ; National Natural Science Foundation of China[U1711265] ; Key Research Program of Frontier Sciences[CAS: QYZDJ-SSW-SYS013]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000453924300008
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/3516]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Huang, Qingming
作者单位1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
2.Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100089, Peoples R China
4.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China
推荐引用方式
GB/T 7714		 Qin, Lei,Zhang, Shengping,Qi, Yuankai,et al. Robust visual tracking via scale-and-state-awareness[J]. NEUROCOMPUTING,2019,329:75-85.
APA Qin, Lei,Zhang, Shengping,Qi, Yuankai,Yao, Hongxun,&Huang, Qingming.(2019).Robust visual tracking via scale-and-state-awareness.NEUROCOMPUTING,329,75-85.
MLA Qin, Lei,et al."Robust visual tracking via scale-and-state-awareness".NEUROCOMPUTING 329(2019):75-85.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。