Moving Cast Shadow Detection Using Texture Information Mani Ranjbar 1 , Shohreh Kasaei 2 1,2 Department of Computer Engineering, Sharif University of Technology, Tehran, Iran m_ranjbar@ce.sharif.edu , skasaei@sharif.edu Abstract In this paper, we have proposed an efficient moving cast shadow detection using texture features. Texture features are estimated using fractal dimension and is employed to discriminate between shadow region and moving foreground region. Complexity and noise reduction is achieved using wavelet transform which has not been performed before in the literature. Block-wise calculation has accelerated the method. The proposed texture modeling method unlike most other approaches is able to cope with very dark shadows as well as light shadows. Keywords Cast Shadow Detection, Dark Shadow, Texture. 1. Introduction Detection and tracking of moving objects is the core of many applications dealing with image sequences. One of the most important challenges of these applications is identifying the moving object and its cast shadow. Shadows cause serious problems when segmenting and extracting moving objects, due to misclassification of shadow regions as foreground. Shadows can cause object merging, object shape distortion and even object missing. The difficulties associated with shadow detection arise since shadows and objects share two important visual features. First, shadow regions are detectable as foreground areas since they typically differ significantly from the background. Second, shadows have the same motion as the objects casting them. As such, shadow identification is critical both for still images and for image sequences (video) and has become an active research area. Shadow analysis, considered in the context of video data, is typically performed for enhancing the quality of segmentation results instead of deducing some imaging or object parameters. In the literature, shadow detection algorithms are normally associated with techniques for moving object segmentation, such as the ones based on inter-frame differencing [3], background subtraction [4], optical flow [5], statistical point classification [6], feature matching, and tracking [7]. In general, there is a difference between dealing with dark shadows and light shadows. Because, light shadows only affect the intensity of the background and the moving object does not change the background color and texture, while dark shadows change the background intensity, color and texture. As a result, distinguishing between object and its shadow is easier in the scene with light shadow. Most of the available methods give their results for light shadows, but our results are given for quite dark shadows. Moreover, our method uses background texture information which is reliable in quite dark shadow situations. 2. Related Works For moving object segmentation, various methods such as inter-frame differencing [3], background subtraction [4], optical flow [5], statistical point classification [6], feature matching [7], and the forth have been proposed. Background subtraction due to its low complexity and proper accuracy is used more than other methods for movement detection. Success of this method is based on the accurate background modeling. A popular method for background modeling is using single Gaussian distribution as used in [8]. Improved versions have used more than one Gaussian distribution for background modeling [9]. Not completely static background can be modeled better with mixture of Gaussian distribution. Many methods have proposed model parameter estimation approaches. In [10], one of the commonly used approaches for updating Gaussian mixture model is presented. In [11], the number of mixture components is constantly adopted for each pixel. Non-parametric approaches for dealing with limitation of parametric models such as Gaussian assumption for pixels intensity is proposed in [12]. Background modeling is performed using edge features in some methods. In [13], edge histograms are used for background modeling. In some later methods like [14], fusion of edge and intensity are used. To the authors’ best knowledge, [1] is the first method which has used texture information for shadow identification. That method uses local binary pattern