We develop statistical methods which allow effective visual detection, categorization, and
tracking of objects in complex scenes. Such computer vision systems must be robust to wide …

We introduce BM³E, a Conditional Bayesian Mixture of Experts Markov Model, for consistent
probabilistic estimates in discriminative visual tracking. The model applies to problems of …

A new, exemplar-based, probabilistic paradigm for visual tracking is presented. Probabilistic
mechanisms are attractive because they handle fusion of information, especially temporal …

We describe a framework that explicitly reasons about data association to improve tracking
performance in many difficult visual environments. A hierarchy of tracking strategies results …

This paper presents an algorithm for modeling, tracking, and recognizing human faces in
video sequences within one integrated framework. Conventional video-based face …

In many applications of graphical models arising in computer vision, the hidden variables of
interest are most naturally specified by continuous, non-Gaussian distributions. There exist …

Continuous quantities are ubiquitous in models of real-world phenomena, but are
surprisingly difficult to reason about automatically. Probabilistic graphical models such as …

A central problem in computer vision is to track objects as they move and deform in a video
sequence. Stochastic algorithms--in particular, particle filters and the Condensation …

Powerful statistical models that can be learned efficiently from large amounts of data are
currently revolutionizing computer vision. These models possess a rich internal structure …

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 …