An optical flow probabilistic observation model for tracking

In this paper, we define an observation model based on optical flow information to track objects using particle filter algorithms. Although the optical flow information enables us to know the displacement of objects present in a scene, it cannot be used directly to displace an object model since flow calculation techniques lack the necessary precision. In view of the fact that probabilistic tracking algorithms enable imprecise or incomplete information to be handled naturally, these models have been used as a natural means of incorporating flow information into the tracking.

• Type: Conference
• Year: 2003
• Where: ICIP
• Authors: Antonio Garrido Carrillo, José M. Fuertes, Manuel J. Lucena, Nicolas Pérez de la Blanca
• Keywords: Enable Imprecise, Flow Calculation Techniques, ICIP 2003, Image Processing, Optical Flow Information, Particle Filter Algorithms, Probabilistic Tracking Algorithms