Moving cast shadow detection from a Gaussian mixture shadow model
Dept. of Electr. & Comput. Eng., Laval Univ., Que., CanadaDOI: 10.1109/CVPR.2005.233 Conference: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Volume: 2
Moving cast shadows are a major concern for foreground detection algorithms. Processing of foreground images in surveillance applications typically requires that such shadows have been identified and removed from the detected foreground. This paper presents a novel pixel-based statistical approach to model moving cast shadows of non-uniform and varying intensity. This approach uses the Gaussian mixture model (GMM) learning ability to build statistical models describing moving cast shadows on surfaces. This statistical modeling can deal with scenes with complex and time-varying illumination, and prevent false detection in regions where shadows cannot be detected. Gaussian mixture shadow models (GMSM) are automatically constructed and updated over time, are easily added to GMM architecture for foreground detection, and require only a small number of parameters. Results obtained with different scene types show the robustness of the approach.
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- "Therefore, shadow detection is of great practical significance in computer vision and pattern recognition. For shadow detection on images, a high proportion of shadow detection methods focus on detecting moving shadows       . "
- "Since some of these color spaces ignore the luminance components of the color, the resulting models become sensitive to noise. In a 'local' shadow model  independent shadow processes are proposed for each pixel. The local shadow parameters are trained using a second mixture model similarly to the background in . "
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- "The application of statistics is somewhat related to the method in , where a dynamic conditional Markov random field is used to detect shadows and perform background subtraction in indoor scenes. In , the authors employ a Gaussian mixture model to characterise the moving cast shadows on the surfaces across the scene. "
ABSTRACT: In this paper, we present a method to model shadows in outdoor scenes. Here, we note that the shadow areas correspond to the diffuse skylight which arises from the scattering of the sunlight by particles in the atmosphere. This yields a treatment in which shadows in the image can be viewed as a linear combination of scattered light obeying Rayleigh scattering and Mie theory. This allows for the computation of a ratio which permits casting the problem of recovering the shadowed areas in the image into a clustering setting making use of active contours. This also opens-up the formulation of a metric that can be used to assess the degree upon which the scene is overcast. We illustrate the utility of the method for purposes of detecting shadows in real-world imagery, provide time complexity results and compare against a number of alternatives elsewhere in the literature.Pattern Recognition Letters 11/2013; DOI:10.1016/j.patrec.2013.10.020 · 1.55 Impact Factor