Foreground Object Detection Using Top-Down Information Based on EM Framework

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	Foreground Object Detection Using Top-Down Information Based on EM Framework
	Liu, Zhou; Huang, Kaiqi; Tan, Tieniu
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2012-09-01
卷号	21 期号:9 页码:4204-4217
文章类型	Article
摘要	In this paper, we present a novel foreground object detection scheme that integrates the top-down information based on the expectation maximization (EM) framework. In this generalized EM framework, the top-down information is incorporated in an object model. Based on the object model and the state of each target, a foreground model is constructed. This foreground model can augment the foreground detection for the camouflage problem. Thus, an object's state-specific Markov random field (MRF) model is constructed for detection based on the foreground model and the background model. This MRF model depends on the latent variables that describe each object's state. The maximization of the MRF model is the M-step in the EM framework. Besides fusing spatial information, this MRF model can also adjust the contribution of the top-down information for detection. To obtain detection result using this MRF model, sampling importance resampling is used to sample the latent variable and the EM framework refines the detection iteratively. Besides the proposed generalized EM framework, our method does not need any prior information of the moving object, because we use the detection result of moving object to incorporate the domain knowledge of the object shapes into the construction of top-down information. Moreover, in our method, a kernel density estimation (KDE)-Gaussian mixture model (GMM) hybrid model is proposed to construct the probability density function of background and moving object model. For the background model, it has some advantages over GMM- and KDE-based methods. Experimental results demonstrate the capability of our method, particularly in handling the camouflage problem.
关键词	Background Model Expectation Maximization (Em) Framework Foreground Detection Markov Random Fields (Mrfs)
WOS标题词	Science & Technology ; Technology
关键词[WOS]	BACKGROUND SUBTRACTION ; ENERGY MINIMIZATION ; GRAPH CUTS ; TRACKING ; SURVEILLANCE ; ALGORITHM ; VISION ; SYSTEM ; MODEL
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:000307896800031
引用统计	被引频次：55[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3789
专题	模式识别实验室
作者单位	Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Liu, Zhou,Huang, Kaiqi,Tan, Tieniu. Foreground Object Detection Using Top-Down Information Based on EM Framework[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2012,21(9):4204-4217.
APA	Liu, Zhou,Huang, Kaiqi,&Tan, Tieniu.(2012).Foreground Object Detection Using Top-Down Information Based on EM Framework.IEEE TRANSACTIONS ON IMAGE PROCESSING,21(9),4204-4217.
MLA	Liu, Zhou,et al."Foreground Object Detection Using Top-Down Information Based on EM Framework".IEEE TRANSACTIONS ON IMAGE PROCESSING 21.9(2012):4204-4217.