Hybrid Codebook Model for Foreground Object Segmentation and Shadow/Highlight Removal

• Published in: Journal of Information Science and Engineering Vol. 30; no. 6; pp. 1965 - 1984
• Main Authors: 孫伊庭(I-Ting Sun), 徐士中(Shih-Chung Hsu), 黃仲陵(Chung-Lin Huang)
• Format: Journal Article
• Language: English
• Published: Taipei 社團法人中華民國計算語言學學會 01.11.2014; Institute of Information Science, Academia sinica
• Subjects: Applied sciences; Artificial intelligence; Benchmarking; Coding, codes; Computer science; control theory; systems; Ellipsoids; Exact sciences and technology; Gaussian; Illumination; Information, signal and communications theory; Pattern recognition. Digital image processing. Computational geometry; Pixels; Segmentation; Shadows; Signal and communications theory; Subtraction; Telecommunications and information theory; mixture of Gaussians (MOG) model; background subtraction; ellipsoidal codeword; codebook (CB) model; shadow removal; Image subtraction; Event detection; Image processing; Shadow; Modeling; Codebook; Illumination; Hybrid model; Dynamic model; Mixed distribution; Computer vision; Shadow method; Real time; Depth of field; Image segmentation; Luminance; Gaussian process; ABackground; Scene analysis; Ellipsoid
• ISSN: 1016-2364
• DOI: 10.6688/JISE.2014.30.6.16

Real-time foreground object extraction is an important subject for computer vision applications. Model-based background subtraction methods have been used to extract the foreground objects. Different from previous methods, this paper introduces a hybrid codebook-based background subtraction method by combining the mixture of Gaussian (MOG) with the codebook (CB) method. We propose an ellipsoid CB model for modeling the dynamic background with highlight and shadow, and develop a modified shadow/highlight removal method to overcome the influence of illumination change. Our method can avoid extracting the false foreground pixels (e.g., dark background) or missing the real foreground pixels (e.g., bright foreground). Finally, we have done two experiments to compare the performance of our method with the others based on [18] and the change detection benchmark dataset provided in CVPR 2011, respectively.