Moving cast shadow detection using online sub-scene shadow modeling and object inner-edges analysis |
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| Affiliation: | 1. School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China;2. National Key Lab of Science and Technology on Multi-spectral Information Processing, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China;1. LAboratoire du Traitement du Signal et de l’Image (LATSI), Department of Electronic, Faculty of Technology, University Saad Dahlab of Blida, Algeria;2. ICUBE, Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie, University of Strasbourg, E.Phot Group, France;1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China;2. School of Software, Sun Yat-sen University, Guangzhou 510006, China;1. Department of Electrical and Computer Engineering, Concordia University, Montréal, QC H3G 2W1, Canada;2. Concordia Institute for Information Systems Engineering, Concordia University, Montréal, QC H3G 2W1, Canada |
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| Abstract: | In this paper, we propose an adaptive and accurate moving cast shadow detection method employing online sub-scene shadow modeling and object inner-edges analysis for applications of static-camera video surveillance. To describe shadow appearance more accurately, the proposed method builds adaptive online shadow models for sub-scenes with different conditions of irradiance and reflectance. The online shadow models are learned by utilizing Gaussian functions to fit the significant peaks of accumulating histograms, which are calculated from Hue, Saturation and Intensity (HSI) difference of moving objects between background and foreground. Additionally, object inner-edges analysis is adopted to reject camouflages, which are misclassified foreground regions that are highly similar to shadows. Finally, the main shadow regions are expanded to recycle the misclassified shadow pixels based on local color constancy. The proposed algorithm can adaptively handle the shadow appearance changes and camouflages without prior information about illuminations and scenarios. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods. |
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| Keywords: | Moving cast shadow detection Video surveillance Foreground extraction Sub-scene shadow modeling Graph-cut Object inner-edges analysis Local color constancy Shadow expanding |
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