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Online Appearance Model Learning and Generation for Adaptive Visual Tracking

文献类型：期刊论文

作者Wang, Peng; Qiao, Hong
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2011-02-01
卷号21期号:2页码:156-169
关键词Adaptive visual tracking appearance variation collaborative models gradual drift model learning
英文摘要Several adaptive visual tracking algorithms have been recently proposed to capture the varying appearance of target. However, adaptability may also result in the problem of gradual drift, especially when the target appearance changes drastically. This paper gives some theoretical principles for online learning of target model, and then presents a novel adaptive tracking algorithm which is able to effectively cope with drastic variations in target appearance and resist gradual drift. Once target is localized in each frame, the patches sampled from target observation are first classified into foreground and background using an effective classifier. Then the adaptive, pure and time-continuous target model is extracted online through two processes: absorption process and rejection process, through which only the reliable features with high separability are absorbed in the new target model, while the "dangerous" features which may cause interfusion of background patterns are rejected. To minimize the influence of background and keep the temporal continuity of target model, two collaborative models dominant model and continuous model are designed. The proposed learning and generation mechanisms of target model are finally embedded in an adaptive tracking system. Experimental results demonstrate the robust performance of the proposed algorithm under challenging conditions.
WOS标题词Science & Technology ; Technology
类目[WOS]Engineering, Electrical & Electronic
研究领域[WOS]Engineering
关键词[WOS]OBJECT TRACKING ; ROBUST TRACKING ; FILTER ; SELECTION ; FEATURES ; COLOR
收录类别SCI
语种英语
WOS记录号WOS:000287867600005
源URL[http://ir.ia.ac.cn/handle/173211/3018]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位Chinese Acad Sci, Inst Automat, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Peng,Qiao, Hong. Online Appearance Model Learning and Generation for Adaptive Visual Tracking[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2011,21(2):156-169.
APA Wang, Peng,&Qiao, Hong.(2011).Online Appearance Model Learning and Generation for Adaptive Visual Tracking.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,21(2),156-169.
MLA Wang, Peng,et al."Online Appearance Model Learning and Generation for Adaptive Visual Tracking".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 21.2(2011):156-169.

入库方式： OAI收割

来源：自动化研究所

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