Selecting the Color Space for Self-Organizing Map Based Foreground Detection in Video

Detecting foreground objects on scenes is a fundamental task in computer vision and the used color space is an important election for this task. In many situations, especially on dynamic backgrounds, neither grayscale nor RGB color spaces represent the best solution to detect foreground objects. Other standard color spaces, such as YCbCr or HSV, have been proposed for background modeling in the literature; although the best results have been achieved using diverse color spaces according to the application, scene, algorithm, etc. In this work, a color space and a color component weighting selection process are proposed to detect foreground objects in video sequences using self-organizing maps. Experimental results are also provided using well known benchmark videos.