Moving cast shadow detection using online sub-scene shadow modeling and object inner-edges analysis

• Published in: Journal of visual communication and image representation 2014-07, Vol.25 (5), p.978-993
• Main Authors: Wang, Jun, Wang, Yuehuan, Jiang, Man, Yan, Xiaoyun, Song, Mengmeng
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Camouflage; Computer science; control theory; systems; Construction; Exact sciences and technology; Foreground extraction; Graph-cut; Illumination; Irradiance; Local color constancy; Mathematical models; Moving cast shadow detection; Object inner-edges analysis; Online; Pattern recognition. Digital image processing. Computational geometry; Shadow expanding; Shadows; Sub-scene shadow modeling; Video surveillance
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2014.02.015

•We proposed an accurate and adaptive method for moving cast shadow detection.•We develop sub-scene shadow models to describe shadow appearance more accurately.•We employ object inner-edges analysis and shadow expanding for improvement.•Our method can adaptively handle shadow appearance changes and camouflages.•The proposed method outperforms some state-of-the-art methods. 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.