Improving Contour Tracker through Evolutionary Optimization

Tracking contours in an image sequence is a challenging task. Tracking algorithms based on particle filter have been proposed for this nonlinear problem. But, contour trackers often collapse due to the sample impoverishment of the traditional particle filter. In this paper, we integrate evolutionary optimization into particle filter, and it is applied to visual contour tracking. The impoverishment problem can be prevented using crossover and mutation operation. Moreover, the re-sampling process is replaced by selection operation. Particles can be redistributed to the local modes with the evolution of the particle population. Experimental results on some recorded videos demonstrate the proposed tracker has the better performance for the changed contour and the clutter.