期刊
PATTERN RECOGNITION
卷 59, 期 -, 页码 112-125出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2016.01.031
关键词
Background subtraction (BS); GMM; ViBe; CB; Dynamic background estimation; Complementary learning
资金
- Shenzhen Oversea High Talent Innovative Fund [KQCX20140521161756231]
- National Natural Science Foundation of China (NSFC) [61527808]
- Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education [30920140122006]
Change and motion detection plays a basic and guiding role in surveillance video analysis. Since most outdoor surveillance videos are taken in native and complex environments, these static backgrounds change in some unknown patterns, which make perfect foreground extraction very difficult. This paper presents two universal modifications for pixel-wise foreground/background segmentation: dynamic background estimation and complementary learning. These two modifications are embedded in three classic background subtraction algorithms: probability based background subtraction (Gaussian mixture model, GMM), sample based background subtraction (visual background extractor, ViBe) and code words based background subtraction (code book, CB). Experiments on several popular public datasets prove the effectiveness and real-time performance of the proposed method. Both GMM and CB with the proposed modifications have better performance than the original versions. Especially, ViBe with the modifications outperforms some state-of-art algorithms presented on the CHANGEDETECTION website. (C) 2016 Elsevier Ltd. All rights reserved.
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