Robust Online Learned Spatio-Temporal Context Model for Visual Tracking
Wen, Longyin1,2; Cai, Zhaowei1,2; Lei, Zhen1,2; Yi, Dong1,2; Li, Stan Z.1,2
2014-02-01
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
卷号23期号:2页码:785-796
文章类型Article
摘要Visual tracking is an important but challenging problem in the computer vision field. In the real world, the appearances of the target and its surroundings change continuously over space and time, which provides effective information to track the target robustly. However, enough attention has not been paid to the spatio-temporal appearance information in previous works. In this paper, a robust spatio-temporal context model based tracker is presented to complete the tracking task in unconstrained environments. The tracker is constructed with temporal and spatial appearance context models. The temporal appearance context model captures the historical appearance of the target to prevent the tracker from drifting to the background in a long-term tracking. The spatial appearance context model integrates contributors to build a supporting field. The contributors are the patches with the same size of the target at the key-points automatically discovered around the target. The constructed supporting field provides much more information than the appearance of the target itself, and thus, ensures the robustness of the tracker in complex environments. Extensive experiments on various challenging databases validate the superiority of our tracker over other state-of-the-art trackers.
关键词Visual Tracking Spatio-temporal Context Multiple Subspaces Learning Online Boosting
WOS标题词Science & Technology ; Technology
关键词[WOS]OBJECTS
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000329581800023
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/8032
专题模式识别国家重点实验室_生物识别与安全技术研究
作者单位1.Chinese Acad Sci, Inst Automat, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Wen, Longyin,Cai, Zhaowei,Lei, Zhen,et al. Robust Online Learned Spatio-Temporal Context Model for Visual Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(2):785-796.
APA Wen, Longyin,Cai, Zhaowei,Lei, Zhen,Yi, Dong,&Li, Stan Z..(2014).Robust Online Learned Spatio-Temporal Context Model for Visual Tracking.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(2),785-796.
MLA Wen, Longyin,et al."Robust Online Learned Spatio-Temporal Context Model for Visual Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.2(2014):785-796.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Wen, Longyin]的文章
[Cai, Zhaowei]的文章
[Lei, Zhen]的文章
百度学术
百度学术中相似的文章
[Wen, Longyin]的文章
[Cai, Zhaowei]的文章
[Lei, Zhen]的文章
必应学术
必应学术中相似的文章
[Wen, Longyin]的文章
[Cai, Zhaowei]的文章
[Lei, Zhen]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

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。