Submitted by Jia-Bin Huang on 2009, June 16 - 19:14.301 views | 0 comments | 11 votes
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Moving Cast Shadow Detection using Physics-based Features

  • Moving Cast Shadow Detection using Physics-based Features
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Cast shadows induced by moving objects often cause serious problems to many vision applications. We present in this paper an online statistical learning approach to model the background appearance variations under cast shadows. Based on the bi-illuminant (i.e. direct light sources and ambient illumination) dichromatic reflection model, we derive physics-based color features under the assumptions of constant ambient illumination and light sources with common spectral power distributions. We first use one Gaussian Mixture Model (GMM) to learn the color features, which are constant regardless of the background surfaces or illuminant colors in a scene. Then, we build up one pixel-based GMM for each pixel to learn the local shadow features. To overcome the slow convergence rate in the conventional GMM learning, we update the pixel-based GMMs through confidence-rated learning. The proposed method can rapidly learn model parameters in an unsupervised way and adapt to illumination conditions or environment changes. Furthermore, we demonstrate that our method is robust to scenes with few foreground activities and videos captured at low or unsteady frame rates.
Jia-Bin Huang and Chu-Song Chen
CVPR - 2009
IEEE

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Keywords Cast shadow detection | computer vision | CVPR 2009 | moving object segmentation | visual surveillance |
Category Computer Science |


AttachmentsSize
CVPR09 Poster Final.pdf1.27 MB
Added 16 Jun 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Jia-Bin Huang and Chu-Song Chen
Attachments 1 file(s)

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