CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术研究
Moving Object Detection Revisited: Speed and Robustness
Han, Hong1; Zhu, Jianfei2,3; Liao, Shengcai3; Lei, Zhen3; Li, Stan Z.3
AbstractThe 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.
KeywordBlock Based Integrated Information Moving Object Object Detection Single Histogram Model
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000357616000002
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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
Recommended Citation
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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