Change detection using a statistical model in an optimally selected color space

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We present a new noise model for color channels for statistical change eletection. Based on this noise modeling, we estimate the distribution of Euclidean distances between the pixel colors of the background image and those of the foreground image. The optimal threshold for change detection is automatically determined using the estimated distribution. We show that our noise modeling is appropriate for various color spaces. Because the detection results differ according to the color space, we utilize the expected number of error pixels to select the appropriate color space for our method. Even if we detect changes based on the optimal threshold in a properly selected color space, there will inevitably be some false classifications. To reject these erroneous cases, we adopt graph cuts that efficiently minimize the global energy while taking into account the effect of neighboring pixels. To validate the proposed method, we show experimental results for a large number of images including indoor and outdoor scenes with complex clutter. (c) 2008 Elsevier Inc. All rights reserved.
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Issue Date
2008-12
Language
English
Article Type
Article
Keywords

IMAGE SEQUENCES; ILLUMINATION; VIDEO

Citation

COMPUTER VISION AND IMAGE UNDERSTANDING, v.112, pp.231 - 242

ISSN
1077-3142
DOI
10.1016/j.cviu.2008.05.001
URI
http://hdl.handle.net/10203/20631
Appears in Collection
EE-Journal Papers(저널논문)
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