Moving Object Detection Revisited: Speed and Robustness

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Moving Object Detection Revisited: Speed and Robustness
	Han, Hong 1; Zhu, Jianfei 2,3; Liao, Shengcai3 ; Lei, Zhen3 ; Li, Stan Z.3
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2015-06-01
卷号	25 期号:6 页码:910-921
文章类型	Article
摘要	The detection of moving objects in videos is very important in many video processing applications, and background modeling is often an indispensable process to achieve this goal. Most of the traditional background modeling methods utilize color or texture information. However, color information is sensitive to illumination variations and texture information cannot be utilized to separate smooth foreground from smooth background in most cases. Achieving good performance in terms of high foreground detection accuracy and low computational cost is also challenging. In this paper, we propose a new integration framework of texture and color information for background modeling, in which the foreground decision equation includes three parts (one part for color information, one part for texture information, and the left part for the integration of color and texture information). This framework is able to combine the advantages of texture and color features while inhibiting their disadvantages as well. Moreover, we propose a block-based method to accelerate the background modeling. In particular, in the texture information modeling process, a single histogram model is established for each block whose bins indicate the occurrence probabilities of different patterns, which is different from the traditional multihistogram model for block-based background modeling, and then dominant background patterns are selected to calculate the background likelihood of new coming blocks. Dynamic background and multimodal problems can be handled through this technique. To evaluate the foreground detection performance reasonably, a new quality measure is proposed. Extensive experiments on various challenging videos validate the effectiveness of the proposed method over state-of-the-art methods.
关键词	Block Based Integrated Information Moving Object Object Detection Single Histogram Model
WOS标题词	Science & Technology ; Technology
收录类别	SCI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000357616000002
引用统计	被引频次：20[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/173211/8868
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
作者单位	1.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 2.Alibaba Grp, Hangzhou, Zhejiang, Peoples R China 3.Chinese Acad Sci, Natl Lab Pattern Recognit, Ctr Biometr & Secur Res, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Han, Hong,Zhu, Jianfei,Liao, Shengcai,et al. Moving Object Detection Revisited: Speed and Robustness[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2015,25(6):910-921.
APA	Han, Hong,Zhu, Jianfei,Liao, Shengcai,Lei, Zhen,&Li, Stan Z..(2015).Moving Object Detection Revisited: Speed and Robustness.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,25(6),910-921.
MLA	Han, Hong,et al."Moving Object Detection Revisited: Speed and Robustness".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 25.6(2015):910-921.

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