Integration of deformable contours and a multiple hypotheses Fisher color model for robust tracking in varying illuminant environments

In this paper, we propose a new technique to perform figure-ground segmentation in image sequences of moving objects under varying illumination conditions. Unlike most of the algorithms that adapt color, there is not the assumption of smooth change of the viewing conditions. To cope with this, we propose the use of a new colorspace that maximizes the foreground/background class separability based on the 'Linear Discriminant Analysis' method. Moreover, we introduce a technique that formulates multiple hypotheses about the next state of the color distribution (some of these hypotheses take into account small and gradual changes in the color model and others consider more abrupt and unexpected variations) and the hypothesis that generates the best object segmentation is used to remove noisy edges from the image. This simplifies considerably the final step of fitting a deformable contour to the object boundary, thus allowing a standard snake formulation to successfully track non-rigid contours. In the same manner, the contour estimate is used to correct the color model. The integration of color and shape is done in a stage called 'sample concentration', introduced as a final step to the well-known CONDENSATION algorithm