A fuzzy approach for multi-feature pedestrian tracking with particle filter

Particle filter is one of the best methods of object tracking in video sequences. Particle filter usually is used with only one feature. In this paper, we propose a novel method for multi-feature object tracking in a particle filter framework. A fuzzy inference system by which reliability of features can be measured has been designed. This is done based on observations diversity and spatial scattering of particles. The features are combined in proportion to their reliabilities. Efficiency of our algorithm is demonstrated using color, edge and texture features. Experimental results over a set of real-world sequences show that our methodpsilas performance is better than some other solutions proposed for feature weighting.