An optimal fuzzy system for feature reliability measuring in particle filter-based object tracking

In this paper, a fuzzy inference system by which reliability of features can be measured is designed. The reliability determines discriminative power of a feature in separating target from background. We focus our attention on design of membership functions. With a rational explanation on available information over a particle filter-base tracking process, we infer a coarse estimation of membership functions. It follows with a fine-tuning stage by using genetic algorithm. Color, edge, texture and TED are used in current work but the extension to a wider number of features is straightforward.