Increasingly, for many application areas, it is becoming important to include elements of
nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a …

During the last two decades there has been a growing interest in Particle Filtering (PF).
However, PF suffers from two long-standing problems that are referred to as sample …

The key limiting factor in graphical model inference and learning is the complexity of the
partition function. We thus ask the question: what are the most general conditions under …

A new approach toward target representation and localization, the central component in
visual tracking of nonrigid objects, is proposed. The feature histogram-based target …

Pedestrian detection is a rapidly evolving area in computer vision with key applications in
intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to …

Mobile robot localization is the problem of determining a robot's pose from sensor data. This
article presents a family of probabilistic localization algorithms known as Monte Carlo …

In this self-contained survey/review paper, we systematically investigate the roots of
Bayesian filtering as well as its rich leaves in the literature. Stochastic filtering theory is …

Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has
proven very successful for non-linear and non-Gaussian estimation problems. The article …

Color-based trackers recently proposed in [3, 4, 5] have been proved robust and versatile for
a modest computational cost. They are especially appealing for tracking tasks where the …

The problem of tracking a varying number of non-rigid objects has two major difficulties.
First, the observation models and target distributions can be highly non-linear and non …