Robustness to changes in illumination conditions as well as viewing perspectives is an important requirement form any computer vision applications. One of the key factors in enhancing the robustness of dynamic scene analysis that of accurate and reliable means for shadow detection. Shadow detection is critical for correct object detection in image sequences. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches still lacking. In this paper, the full range of problems underlying the shadow detection are identified and discussed. We classify the proposed solutions to this problem using axonomy off our main classes, called deterministic model and non-model based and statistical parametric and non-parametric. Novel quantitative (detection and discrimination accuracy) and qualitative metrics (scene and object independence, exibility to shadow situations and robustness to noise) are proposed to evaluate these Classes of algo-rithms on a benchmark suite of indoor and outdoor videosequences.
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