Efficient Object Tracking by Incremental Self-Tuning Particle Filtering on the Affine Group

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	Efficient Object Tracking by Incremental Self-Tuning Particle Filtering on the Affine Group
	Li, Min; Tan, Tieniu; Chen, Wei; Huang, Kaiqi
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2012-03-01
卷号	21 期号:3 页码:1298-1313
关键词	Incremental self-tuning particle filtering (ISPF) pose estimator (PE) sparse sampling visual tracking
英文摘要	We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group, which can find the optimal state in a chainlike way with a very small number of particles. Unlike traditional particle filtering, which only relies on random sampling for state optimization, ISPF incrementally draws particles and utilizes an online-learned pose estimator (PE) to iteratively tune them to their neighboring best states according to some feedback appearance-similarity scores. Sampling is terminated if the maximum similarity of all tuned particles satisfies a target-patch similarity distribution modeled online or if the permitted maximum number of particles is reached. With the help of the learned PE and some appearance-similarity feedback scores, particles in ISPF become "smart" and can automatically move toward the correct directions; thus, sparse sampling is possible. The optimal state can be efficiently found in a step-by-step way in which some particles serve as bridge nodes to help others to reach the optimal state. In addition to the single-target scenario, the "smart" particle idea is also extended into a multitarget tracking problem. Experimental results demonstrate that our ISPF can achieve great robustness and very high accuracy with only a very small number of particles.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	REAL-TIME TRACKING ; VISUAL TRACKING ; SCALE
收录类别	SCI
语种	英语
WOS记录号	WOS:000300510800032
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3791]
专题	自动化研究所_智能感知与计算研究中心
作者单位	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Min,Tan, Tieniu,Chen, Wei,et al. Efficient Object Tracking by Incremental Self-Tuning Particle Filtering on the Affine Group[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2012,21(3):1298-1313.
APA	Li, Min,Tan, Tieniu,Chen, Wei,&Huang, Kaiqi.(2012).Efficient Object Tracking by Incremental Self-Tuning Particle Filtering on the Affine Group.IEEE TRANSACTIONS ON IMAGE PROCESSING,21(3),1298-1313.
MLA	Li, Min,et al."Efficient Object Tracking by Incremental Self-Tuning Particle Filtering on the Affine Group".IEEE TRANSACTIONS ON IMAGE PROCESSING 21.3(2012):1298-1313.