A Probabilistic Background Model for Tracking

A new probabilistic background model based on a Hidden Markov Model is presented. The hidden states of the model enable discrimination between foreground, background and shadow. This model functions as a low level process for a car tracker.A particle filter is employed as a stochastic filter for the car tracker. The use of a particle filter allows the incorporation of the information from the low level process via importance sampling. A novel observation density for the particle filter which models the statistical dependence of neighboring pixels based on a Markov random field is presented. The effectiveness of both the low level process and the observation likelihood are demonstrated.

• Type: Conference
• Year: 2000
• Where: ECCV
• Authors: Jens Rittscher, Jien Kato, Sébastien Joga, Andrew Blake
• Keywords: Computer Vision, ECCV 2000, Hidden Markov Model, Low Level Process, Particle Filter, Probabilistic Background Model, Tracker.A Particle Filter