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A shadow detection method via online self-modeling

In this paper, we propose an accurate shadow detection method via online self-modeling without tuning any feature threshold and manual labeling work. A primary classification is obtained from the fusion of classification results of a weak classifier like a low-value chromatic threshold technique and the online learned shadow generative model. Then object skeleton property and shadow’s spatial structure characters are considered to remove the camouflages and output the final classification result, the detected shadow pixels are used as training samples in the learning phase without manually labeling work. Online shadow model is learned by using Gaussian functions to fit the histograms of differential Hue, Saturation, and Intensity between background pixels and corresponding shadow pixels. Experiments indicate that the proposed method achieve both high detective and discriminative rates and outperform the approaches which need tuning thresholds when applied scene changes in accuracy and robustness.