A Robust Background Subtraction and Shadow Detection

This paper presents a novel algorithm for detecting moving objects from a static background scene that contains shading and shadows using color images. Although the background subtraction technique has been used for years in many vision systems as a preprocessing step for object detection and tracking, most of these algorithms are susceptible to both global and local illumination changes such as shadows and highlights. These cause the consequent processes, e.g. tracking, recognition, etc., to fail. This problem is the underlying motivation of our work. We develop a robust and efficiently computed background subtraction algorithm that is able to cope with local illumination change problems, such as shadows and highlights, as well as global illumination changes. Experimental results, which demonstrate the system’s performance, are also shown.

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