MRF-based Particle Filters for Multi-touch Tracking and Gesture Likelihoods

Multi-touch tracking algorithm requires maintaining separate identities for multi-touch points, however, it fails when independent particle filter for each object is kidnapped by neighboring targets. This is called the hijacking problem. The motion model using Markov random field (MRF) has been proposed for avoiding this problem by lowering the weight of particles which are close to neighboring touch points. This paper improves the MRF-based particle filters for multi-touch tracking by optimizing the distance of neighboring touch points to reduce hijacking problem. In experiments, the optimum distance is around 80 pixels, which exhibits highly robust and optimized multi-touch tracking. Additionally we discuss about the simultaneous estimation of gesture likelihoods with MRF potentials from the tracking results.