Multiple cue adaptive tracking of deformable objects with Particle Filter

This paper presents a tracking algorithm based on a sequential importance sampling (SIS) particle filter scheme followed by a resampling strategy where shape and color cues are exploited to handle deformable objects. The state vector is composed by a set of corners and it enables to jointly describe position and shape of the target. Mean Shift trackers, applied to color cues associated to state subspaces, are employed to predict the target global motion. An adaptive system noise is defined based on this information to cope with local deformations. The updating procedure is accomplished by a shape matching technique. Experimental results prove the effectiveness of the proposed approach with respect to simple deformations, partial occlusions and moving camera.