###### Abstract  
Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, we present a novel moving cast shadow detection framework based on the extreme learning machine (ELM) to efficiently distinguish shadow points from the foreground object. First, according to the physical model of shadows, pixel-level features of different channels in different color spaces and region-level features derived from the spatial correlation of neighboring pixels are extracted from the foreground. Second, an ELM-based classification model is developed by labelled shadow and unlabelled shadow points, which is able to rapidly distinguish the points in the new input whether they belong to shadows or not. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on two publicly common datasets including 13 different scenes demonstrate that the performance of the proposed framework is superior to representative state-of-the-art methods. m 2019 9 5042 2019 22 2019 22019 2  
-based methods, physical-based methods, geometry-based methods and texture-based methods, according to the type of shadow features. Regardless of the above categories, the process of shadow detection mainly consists of two stages: feature extraction and classification. The commonly applied shadow features are divided into pixel-level features and region-level features based on shadow properties [4]. In particular, the pixel-level feature is the value obtained by different channels in different color spaces, while the region-level feature is extracted statistically in terms of spatial correlations of neighboring pixels. A significant property of a shadow is that the shadow is darker than that of the corresponding background and maintains constant chromaticity. Based on the property, the widely applied pixel-level features are intensity and chromaticity. [PERSON] et al. [7] first proposed a chromaticity-based method to detect shadows by calculating the change rates of three components in HSV color space. Thereafter, many color space-based methods were developed to exploit this property for shadow detection, such as RGB [8, 9], \(c_{1}c_{2}c_{3}\)[10], YCbCr [11], normalized RGB [12], YUV [13], HSI [14] or combination among of them [15, 16]. However, just only intensity and chromaticity features may not be able to accurately separate shadows from foregrounds when moving shadows and moving objects both have darker colors. Moreover, pixel-based methods are sensitive to noise. To solve these problems, the other property of a shadow is exploited to perform shadow detection, which assumes that the texture of the shadow is similar to the background and it is different from the foreground. According to the texture consistency, the region-level feature is described by spatial correlations of neighboring pixels, which is also called the statistical feature. The local texture descriptors, which are robust to noise and illumination variation, have been adopted widely to detect shadows, such as Gabor function [17], scale-invariant local ternary patterns (SILTP) [18], discrete wavelet transform (DWT) [19], gradient information [20], non-linear tone-mapping (NTM) [21] or their combinations [22]. These kinds of methods will fail when the texture property is similar between the background and foreground. Obviously, using a single property cannot detect shadows completely. Therefore, moving shadow detection methods with multiple features based on these two properties have attracted extensive attention [23, 24, 25, 26, 27]. In particular, [PERSON] et al.