A probabilistic approach for dynamic state estimation using visual information

Abstract

This work presents a computational framework for the adaptive integration of information from different visual algorithms. The approach takes advantage of the richness of visual information by adaptively considering a variety of visual properties such as color, depth, motion, and shape. Using a probabilistic approach and uncertainty metrics, the resulting framework makes appropriate decisions about the most relevant visual attributes to consider. The framework is based on an agent paradigm. Each visual algorithm is implemented as an agent that adapts its behavior according to uncertainty considerations. These agents act as a group of experts, where each agent has a specific knowledge area. Cooperation among the agents is given by a probabilistic scheme that uses Bayesian inference to integrate the evidential information provided by them. To deal with the inherent no linearity of visual information, the relevant probability distributions are represented using a stochastic sampling approach. The estimation of the state of relevant visual structures is performed using an enhanced version of the particle filter algorithm. This enhanced version includes novel methods to adaptively select the number of samples used by the filter, and to adaptively find a suitable function to propagate the samples. We show the advantages of our approach by applying it to the task of tracking targets in a real video sequence.