Conference Paper

Moving cast shadow detection from a Gaussian mixture shadow model

Dept. of Electr. & Comput. Eng., Laval Univ., Que., Canada
DOI: 10.1109/CVPR.2005.233 Conference: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Volume: 2
Source: DBLP


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.

    • "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 [8] [9] [10] [11] [12] [13] [14]. "
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    ABSTRACT: Successfully detecting shadows in still images is challenging yet has wide applications. Shadow properties and features are very important for shadow detection and processing. The aim of this work is to find some new physical properties of shadows and use them as shadow features to design an effective shadow detection method for outdoor color images. We observe that although the spectral power distribution (SPD) of daylight and that of skylight are quite different, in each channel, the spectrum ratio of the point-wise product of daylight SPD with sRGB color matching functions (CMFs) to the point-wise product of skylight SPD with sRGB CMFs roughly approximates a constant. This further leads to that the ratios of linear sRGB pixel values of surfaces illuminated by daylight (in non-shadow regions) to those illuminated by skylight (in shadow regions) equal to a constant in each channel. Following this observation, we calculated the spectrum ratios under various Sun angles and further found out four new shadow properties. With these properties as shadow features, we developed a simple shadow detection method to quickly locate shadows in single still images. In our method, we classify an edge as a shadow or non-shadow edge by verifying whether the pixel values on both sides of the Canny edges satisfy the three shadow verification criteria derived from the shadow properties. Extensive experiments and comparison show that our method outperforms state-of-the-art shadow detection methods.
    No preview · Article · Oct 2015 · Pattern Recognition
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    • "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 [31] independent shadow processes are proposed for each pixel. The local shadow parameters are trained using a second mixture model similarly to the background in [4]. "

    Full-text · Dataset · Feb 2014
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    • "The application of statistics is somewhat related to the method in [31], where a dynamic conditional Markov random field is used to detect shadows and perform background subtraction in indoor scenes. In [20], the authors employ a Gaussian mixture model to characterise the moving cast shadows on the surfaces across the scene. "
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    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.
    Full-text · Article · Nov 2013 · Pattern Recognition Letters
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