A novel recursive bayesian learning-based method for the efficient and accurate segmentation of video with dynamic background

IEEE Transactions on Image Processing

Volumn 21, Issue 9, 2012, Pages 3865-3876

  Zhu, Qingsong ^a   Song, Zhan ^a,b   Xie, Yaoqin ^a   Wang, Lei ^a  

^a SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Dynamic background; Gaussian mixture model; recursive Bayesian learning; video segmentation

Indexed keywords

BACKGROUND MOTION; BACKGROUND SUBTRACTION; BAYESIAN LEARNING; DYNAMIC BACKGROUND; FOREGROUND REGIONS; GAUSSIAN MIXTURE MODEL; LEARNING-BASED METHODS; LOCAL TEXTURE; RECURSIVE BAYESIAN ESTIMATION; RESEARCH TOPICS; SEGMENTATION ACCURACY; SEGMENTATION QUALITY; VIDEO DATASETS; VIDEO SEGMENTATION;

BAYESIAN NETWORKS; COMPUTER VISION; IMAGE SEGMENTATION; NORMAL DISTRIBUTION;

ALGORITHMS;

EID: 84865430298     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2199504     Document Type: Article

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